Classifying objects with additional measurements

ABSTRACT

A vehicle-assistance system for classifying objects in a vehicle&#39;s surroundings is provided. The system may include at least one memory configured to store classification information for classifying a plurality of objects and at least one processor configured to receive, on a pixel-by-pixel basis, a plurality of measurements associated with LIDAR detection results. The measurements may include at least one of: a presence indication, a surface angle, object surface physical composition, and a reflectivity level. The at least one processor may also be configured to receive, on the pixel-by-pixel basis, at least one confidence level associated with each received measurement, and access the classification information. The at least one processor may further be configured to, based on the classification information and the received measurements with the at least one associated confidence level plurality, identify a of pixels as being associated with a particular object.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of PCT International Application No.PCT/162018/000063, filed Jan. 3, 2018, which claims the benefit ofpriority of U.S. Provisional Patent Application No. 62/441,574, filedJan. 3, 2017; U.S. Provisional Patent Application No. 62/441,578, filedJan. 3, 2017; U.S. Provisional Patent Application No. 62/441,581, filedJan. 3, 2017; U.S. Provisional Patent Application No. 62/441,583, filedJan. 3, 2017; U.S. Provisional Patent Application No. 62/441,606, filedJan. 3, 2017; U.S. Provisional Patent Application No. 62/441,610, filedJan. 3, 2017; U.S. Provisional Patent Application No. 62/441,611, filedJan. 3, 2017; U.S. Provisional Patent Application No. 62/455,627, filedFeb. 7, 2017; U.S. Provisional Patent Application No. 62/456,691, filedFeb. 9, 2017; U.S. Provisional Patent Application No. 62/461,802, filedFeb. 22, 2017; U.S. Provisional Patent Application No. 62/516,694, filedJun. 8, 2017; U.S. Provisional Patent Application No. 62/521,450, filedJun. 18, 2017; U.S. Provisional Patent Application No. 62/560,985, filedSep. 20, 2017; U.S. Provisional Patent Application No. 62/563,367, filedSep. 26, 2017; U.S. Provisional Patent Application No. 62/567,692, filedOct. 3, 2017; U.S. Provisional Patent Application No. 62/589,686, filedNov. 22, 2017; U.S. Provisional Patent Application No. 62/591,409, filedNov. 28, 2017; and U.S. Provisional Patent Application No. 62/596,261,filed Dec. 8, 2017. All of the foregoing applications are incorporatedherein by reference in their entirety.

BACKGROUND I. Technical Field

The present disclosure relates generally to surveying technology forscanning a surrounding environment, and, more specifically, to systemsand methods that use LIDAR technology to detect and classify objects inthe surrounding environment.

II. Background Information

With the advent of driver assist systems and autonomous vehicles,automobiles need to be equipped with systems capable of reliably sensingand interpreting their surroundings, including identifying obstacles,hazards, objects, and other physical parameters that might impactnavigation of the vehicle. To this end, a number of differingtechnologies have been suggested including radar, LIDAR, camera-basedsystems, operating alone or in a redundant manner.

One consideration with driver assistance systems and autonomous vehiclesis an ability of the system to determine surroundings across differentconditions including, rain, fog darkness, bright light, and snow. Alightdetection and ranging system. (LIDAR a/k/a LADAR) is an example oftechnology that can work well in differing conditions, by measuringdistances to objects by illuminating objects with light and measuringthe reflected pulses with a sensor. A laser is one example of alightsource that can be used in a LIDAR system. As with any sensing system,in order for a LIDAR-based sensing system to be fully adopted by theautomotive industry, the system should provide reliable data enablingdetection of far-away objects. Currently, however, the maximumillumination power of LIDAR systems is limited by the need to make theLIDAR systems eye-safe (i.e., so that they will not damage the human eyewhich can occur when a projected light emission is absorbed in the eye'scornea and lens, causing thermal damage to the retina.)

Moreover, extant LIDAR systems generally identify and classify objectsusing distance information determined from reflected pulses. Forexample, such information may be used to construct a point-cloud map (orother 3D map), from which objects may be identified and classified.However, such identification and classification is frequentlyerror-prone and inaccurate, as well as inefficient. The systems andmethods of the present disclosure are directed towards improvingperformance of LIDAR systems, particularly with regards toidentification and classification of objects.

SUMMARY

Embodiments of the present disclosure may therefore improve the accuracyof and/or improve the efficiency of identification and classification ofobjects using a LIDAR. For example, systems of the present disclosuremay detect one or more surface angles of an object based on one or moretemporal distortions in reflection signals. In further embodiments, thesystems of the present disclosure may identify objects usingreflectivity fingerprints, surface angle fingerprints, or other measuredproperties, such as object surface physical composition, ambientillumination measured at a LIDAR dead time, difference in detectioninformation from a previous frame, and confidence levels associated withone or more detection characteristics.

In one embodiment of the present disclosure, a LIDAR system fordetecting a vehicle based on license plate reflectivity may comprise atleast one processor. The at least one processor may be configured toscan a field of view by controlling movement of at least one deflectorat which at least one light source is directed, receive, from at leastone sensor, signals indicative of light reflected from a particularobject in the field of view; detect, based on time of flight in thereceived signals, portions of the particular object in the field of viewthat are similarly spaced from the light source; and determine, based onthe detected portions, at least a first portion having a firstreflectivity corresponding to a license plate, and at least twoadditional spaced-apart portions corresponding to locations on theparticular object other than a location of the first portion. The atleast two additional portions may have reflectivity substantially lowerthan the first reflectivity. The at least one processor may be furtherconfigured to, based on a spatial relationship and a reflectivityrelationship between the first portion and the at least two additionalportions, classify the particular object as a vehicle.

In an embodiment of the present disclosure, a vehicle may comprise abody and at least one processor within the body. The at least oneprocessor may be configured to scan a field of view by controllingmovement of at least one deflector at which at least one light source isdirected; receive, from at least one sensor, reflections signalsindicative of light reflected from a particular object in the field ofview; detect, based on time of flight, portions of the particular objectin the field of view that are similarly spaced from the light source;and identify, based on the detected portions, at least a first portionhaving a first reflectivity, a second portion having a secondreflectivity, and a third portion having a third reflectivity. The atleast second and third portions may have reflectivity substantiallylower than the first reflectivity. The at least one processor may befurther configured to determine a reflectivity fingerprint of theparticular object based on a reflectivity relationship between the firstportion, the second portion, and the third portion; and classify theparticular object based on the determined reflectivity fingerprint ofthe particular object.

In an embodiment of the present disclosure, a method for using LIDAR todetect a vehicle based on license plate reflectivity may comprisescanning a field of view by controlling movement of at least onedeflector at which at least one light source is directed; receiving,from at least one sensor, signals indicative of light reflected from aparticular object in the field of view; detecting, based on time offlight in the received signals, portions of the particular object in thefield of view that are similarly spaced from the light source; anddetermining based on the detected portions, at least a first portionhaving a first reflectivity corresponding to a license plate, and atleast two additional spaced-apart portions corresponding to locations onthe particular object other than a location of the first portion. The atleast two additional portions may have reflectivity substantially lowerthan the first reflectivity. The method may further comprise, based on aspatial relationship and a reflectivity relationship between the firstportion and the at least two additional portions, classifying theparticular object as a vehicle.

In an embodiment of the present disclosure, anon-transitorycomputer-readable storage medium may store instructions that, whenexecuted by at least one processor, cause the at least one processor toperform a method for using LIDAR to detect a vehicle based on licenseplate reflectivity. The method may comprise scanning a field of view bycontrolling movement of at least one deflector at which at least onelight source is directed; receiving, from at least one sensor, signalsindicative of light reflected from a particular object in the field ofview; detecting, based on time of flight in the received signals,portions of the particular object in the field of view that aresimilarly spaced from the light source; and determining based on thedetected portions, at least a first portion having a first reflectivitycorresponding to a license plate, and at least two additionalspaced-apart portions corresponding to locations on the particularobject other than a location of the first portion. The at least twoadditional portions may have reflectivity substantially lower than thefirst reflectivity. The method may further comprise, based on a spatialrelationship and a reflectivity relationship between the first portionand the at least two additional portions, classifying the particularobject as a vehicle.

In an embodiment of the present disclosure, a LIDAR system for use in avehicle may comprise at least one processor. The at least one processormay be configured to control at least one light source for illuminatinga field of view; scan a field of view by controlling movement of atleast one deflector at which the at least one light source is directed;receive, from at least one sensor, reflections signals indicative oflight reflected from an object in the field of view; detect at least onetemporal distortion in the reflections signals; and determine from theat least one temporal distortion an angular orientation of at least aportion of the object.

In an embodiment of the present disclosure, a vehicle may comprise abody and at least one processor within the body. The at least oneprocessor may be configured to control activation of at least one lightsource for illuminating a field of view; scan a field of view bycontrolling movement of at least one deflector at which the at least onelight source is directed receive, from at least one sensor, reflectionssignals indicative of light reflected from an object in the field ofview; detect at least one temporal distortion in the reflectionssignals; and determine from the at least one temporal distortion anangular orientation of at least a portion of the object.

In an embodiment of the present disclosure, a method for using LIDAR todetermine angular orientation of objects in a field of view may comprisecontrolling activation of at least one light source for illuminating afield of view; scanning a field of view by controlling movement of atleast one deflector at which the at least one light source is directed;receiving, from at least one sensor, reflections signals indicative oflight reflected from an object in the field of view; detecting at leastone temporal distortion in the reflections signals; and determining fromthe at least one temporal distortion an angular orientation of at leasta portion of the object.

In an embodiment of the present disclosure, anon-transitorycomputer-readable storage medium may store instructions that, whenexecuted by at least one processor, cause the at least one processor toperform a method for using LIDAR to determine angular orientation ofobjects in a field of view. The method may comprise controllingactivation of at least one light source for illuminating a field ofview; scanning a field of view by controlling movement of at least onedeflector at which the at least one light source is directed; receiving,from at least one sensor, reflections signals indicative of lightreflected from an object in the field of view; detecting at least onetemporal distortion in the reflections signals; and determining from theat least one temporal distortion an angular orientation of at least aportion of the object.

In an embodiment of the present disclosure, a vehicle-assistance systemfor classifying objects in a vehicle's surroundings may comprise atleast one memory configured to store classification information forclassifying a plurality of objects and at least one processor. The atleast one processor may be configured to receive, on a pixel-by-pixelbasis, a plurality of measurements associated with LIDAR detectionresults. The measurements may include at least one of: a presenceindication, a surface angle, object surface physical composition, and areflectivity level. The at least one processor may be further configuredto receive, on the pixel-by-pixel basis, at least one confidence levelassociated with each received measurement; access the classificationinformation; and based on the classification information and thereceived measurements with the at least one associated confidence level,identify a plurality of pixels as being associated with a particularobject.

In an embodiment of the present disclosure, a vehicle-assistance systemfor identifying objects in a vehicle's surroundings may comprise atleast one processor. The at least one processor may be configured toreceive point-cloud information originating from a LIDAR configured toproject light toward the vehicle's surroundings. The point-cloudinformation may be associated with a plurality of data points, and eachdata point may include indications of a three-dimensional location andangular information with respect to a reference plane. The at least oneprocessor may be further configured to construct, from the receivedpoint cloud (PC) information, a point cloud map of the vehicle'ssurroundings. The point cloud map may be indicative of a shape of aparticular object in the vehicle's surroundings and of angularorientations of at least two surfaces of the particular object. The atleast one processor may be further configured to access object-relatedclassification information; and identify the particular object based onthe information from the point cloud map and the object-relatedclassification information.

In an embodiment of the present disclosure, a method for using LIDARsystem to classify objects in a field of view may comprise receiving, ona pixel-by-pixel basis, measurements associated with LIDAR detectionresults. The measurements may include at least one of a presenceindication, a surface angle, and a reflectivity level. The method mayfurther comprise receiving, on the pixel-by-pixel basis, a confidencelevel associated with each received measurement; accessingclassification information for classifying the objects; and based on theclassification information and the received measurements with theassociated confidence level, classifying a plurality of pixels as beingassociated with a particular object.

In an embodiment of the present disclosure, anon-transitorycomputer-readable storage medium may store instructions that, whenexecuted by at least one processor, cause the at least one processor toperform a method for identifying objects in a vehicle's surroundings.The method may comprise receiving point-cloud information originatingfrom a LIDAR configured to project light toward the vehicle'ssurroundings. The point-cloud information may be associated with aplurality of data points, and each data point may include indications ofa three-dimensional location and angular information with respect to areference plane. The method may further comprise constructing, from thereceived point cloud information, a point cloud map of the vehicle'ssurroundings. The point cloud map may be indicative of a shape of aparticular object in the vehicle's surroundings and of angularorientations of at least two surfaces of the particular object. Themethod may further comprise accessing object-related classificationinformation; and identifying the particular object based on theinformation from the point cloud map and the object-relatedclassification information.

In an embodiment of the present disclosure, a vehicle-assistance systemfor classifying objects in a vehicle's surroundings may comprise atleast one memory configured to store classification information forclassifying a plurality of objects and at least one processor. The atleast one processor may be configured to receive a plurality ofdetection results associated with LIDAR detection results. Eachdetection result may include location information, and furtherinformation indicative of at least two of the following detectioncharacteristics: object surface reflectivity; temporal spreading ofsignal reflected from the object; object surface physical composition;ambient illumination measured at a LIDAR dead time; difference indetection information from a previous frame; and confidence levelassociated with another detection characteristic. The at least oneprocessor may be further configured to access the classificationinformation; and based on the classification information and detectionsresults, classify an object in the vehicle's surroundings.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods or processor-executed steps described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1A is a diagram illustrating an exemplary LIDAR system consistentwith disclosed embodiments.

FIG. 1B is an image showing an exemplary output of single scanning cycleof a LIDAR system mounted on a vehicle consistent with disclosedembodiments.

FIG. 1C is another image showing a representation of a point cloud modeldetermined from output of a LIDAR system consistent with disclosedembodiments.

FIG. 2A is a diagram illustrating an example configuration of an exampleprojecting unit in accordance with some embodiments of the presentdisclosure.

FIG. 2B is a diagram illustrating another example configuration of anexample projecting unit in accordance with some embodiments of thepresent disclosure.

FIG. 2C is a diagram illustrating yet another example configuration ofan example projecting unit in accordance with some embodiments of thepresent disclosure.

FIG. 2D is a diagram illustrating a fourth example configuration of anexample projecting unit in accordance with some embodiments of thepresent disclosure.

FIG. 3A is a diagram illustrating an example configuration of an examplescanning unit in accordance with some embodiments of the presentdisclosure.

FIG. 3B is a diagram illustrating another example configuration of anexample scanning unit in accordance with some embodiments of the presentdisclosure.

FIG. 3C is a diagram illustrating yet another example configuration ofan example scanning unit in accordance with some embodiments of thepresent disclosure.

FIG. 3D is a diagram illustrating a fourth example configuration of anexample scanning unit in accordance with some embodiments of the presentdisclosure.

FIG. 4A is a diagram illustrating an example configuration of an examplesensing unit in accordance with some embodiments of the presentdisclosure.

FIG. 4B is a diagram illustrating another example configuration of anexample sensing unit in accordance with some embodiments of the presentdisclosure.

FIG. 4C is a diagram illustrating yet another example configuration ofan example sensing unit in accordance with some embodiments of thepresent disclosure.

FIG. 4D is a diagram illustrating a fourth example configuration of anexample sensing unit in accordance with some embodiments of the presentdisclosure.

FIG. 4E is a diagram illustrating a fifth example configuration of anexample sensing unit in accordance with some embodiments of the presentdisclosure.

FIG. 5A includes four example diagrams illustrating emission patterns ina single frame-time for a single portion of the field of view.

FIG. 5B includes three example diagrams illustrating emission scheme ina single frame-time for the whole field of view.

FIG. 5C is a diagram illustrating the actual light emission projectedtowards and reflections received during a single frame-time for thewhole field of view.

FIG. 6A is a diagram illustrating a first example implementationconsistent with some embodiments of the present disclosure.

FIG. 6B is a diagram illustrating an example emission scheme used in theexample implementation of FIG. 6A.

FIG. 6C is a diagram illustrating an example view range of the exampleimplementation of FIG. 6A.

FIG. 6D is a diagram illustrating a second example implementationconsistent with some embodiments of the present disclosure.

FIG. 7 depicts an exemplary method for detecting a vehicle based onlicense plate reflectivity consistent with some embodiments of thepresent disclosure.

FIG. 8 depicts an exemplary method for classifying vehicles based on areflectivity fingerprint consistent with some embodiments of the presentdisclosure.

FIG. 9 illustrates an example of identification performed using areflectivity fingerprint consistent with some embodiments of the presentdisclosure.

FIG. 10 depicts an exemplary method for detecting an angular orientationof an object consistent with some embodiments of the present disclosure.

FIG. 11 depicts an exemplary method for detecting road-surface markingson roads consistent with some embodiments of the present disclosure.

FIG. 12A illustrates an example of determining a surface angle based onstretching of a returning pulse in time consistent with some embodimentsof the present disclosure.

FIG. 12B illustrates an example of determining surface angles based ondifferences in return time to different quadrants of the sensorsconsistent with some embodiments of the present disclosure.

FIG. 13A illustrates an example of angular orientations being slopes ofa portion of a vehicle consistent with some embodiments of the presentdisclosure.

FIG. 13B illustrates an example of angular orientations being slopes ofa portion of a road consistent with some embodiments of the presentdisclosure.

FIG. 14 illustrates an example of a LIDAR system having a plurality offilters consistent with some embodiments of the present disclosure.

FIG. 15 illustrates an example of a reflection signal spanning a firstduration and having a narrow peak consistent with some embodiments ofthe present disclosure.

FIG. 16 illustrates an example of correlating temporal sequences ofdetected reflection levels and a return-signal hypothesis to provide acorrelation output consistent with some embodiments of the presentdisclosure.

FIG. 17 depicts an exemplary method for classifying objects insurroundings of a vehicle consistent with some embodiments of thepresent disclosure.

FIG. 18 depicts another exemplary method for classifying objects insurroundings of a vehicle consistent with some embodiments of thepresent disclosure.

FIG. 19 depicts yet another exemplary method for classifying objects insurroundings of a vehicle consistent with some embodiments of thepresent disclosure.

FIG. 20 depicts an exemplary method for identifying objects insurroundings of a vehicle consistent with some embodiments of thepresent disclosure.

FIG. 21 depicts a fourth exemplary method for classifying objects insurroundings of a vehicle consistent with some embodiments of thepresent disclosure.

FIG. 22 depicts an example of identification performed using confidencelevels consistent with some embodiments of the present disclosure.

FIG. 23A is a block diagram of an exemplary system for processing cloudpoint information to identify objects in a scene consistent with someembodiments of the present disclosure.

FIG. 23B is a block diagram of another exemplary system for processingcloud point information to identify objects in a scene consistent withsome embodiments of the present disclosure.

FIG. 24 is a flowchart illustrating an exemplary method for objectclassification consistent with some embodiments of the presentdisclosure.

FIG. 25A depicts an exemplary vehicle having a LIDAR system with avibration suppression system consistent with some embodiments of thepresent disclosure.

FIG. 25B depicts an exemplary LIDAR system with a vibration suppressionsystem consistent with some embodiments of the present disclosure.

FIG. 25C depicts exemplary feedback sensors used in a vibrationsuppression system consistent with some embodiments of the presentdisclosure.

FIG. 26A is a cross-section illustrating an exemplary MEMS mirrorenclosed within a housing consistent with some embodiments of thepresent disclosure.

FIG. 26B is a cross-section illustrating another exemplary MEMS mirrorenclosed within a housing consistent with some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

Terms Definitions

Disclosed embodiments may involve an optical system. As used herein, theterm “optical system” broadly includes any system that is used for thegeneration, detection and/or manipulation of light. By way of exampleonly, an optical system may include one or more optical components forgenerating, detecting and/or manipulating light. For example, lightsources, lenses, mirrors, prisms, beam splitters, collimators,polarizing optics, optical modulators, optical switches, opticalamplifiers, optical detectors, optical sensors, fiber optics,semiconductor optic components, while each not necessarily required, mayeach be part of an optical system. In addition to the one or moreoptical components, an optical system may also include other non-opticalcomponents such as electrical components, mechanical components,chemical reaction components, and semiconductor components. Thenon-optical components may cooperate with optical components of theoptical system. For example, the optical system may include at least oneprocessor for analyzing detected light.

Consistent with the present disclosure, the optical system may be aLIDAR system. As used herein, the term “LIDAR system” broadly includesany system which can determine values of parameters indicative of adistance between a pair of tangible objects based on reflected light. Inone embodiment, the LIDAR system may determine a distance between a pairof tangible objects based on reflections of light emitted by the LIDARsystem. As used herein, the term “determine distances” broadly includesgenerating outputs which are indicative of distances between pairs oftangible objects. The determined distance may represent the physicaldimension between a pair of tangible objects. By way of example only,the determined distance may include a line of flight distance betweenthe LIDAR system and another tangible object in a field of view of theLIDAR system. In another embodiment, the LIDAR system may determine therelative velocity between a pair of tangible objects based onreflections of light emitted by the LIDAR system. Examples of outputsindicative of the distance between a pair of tangible objects include: anumber of standard length units between the tangible objects (e.g.number of meters, number of inches, number of kilometers, number ofmillimeters), a number of arbitrary length units (e.g. number of LIDARsystem lengths), a ratio between the distance to another length (e.g. aratio to a length of an object detected in a field of view of the LIDARsystem), an amount of time (e.g. given as standard unit, arbitrary unitsor ratio, for example, the time it takes light to travel between thetangible objects), one or more locations (e.g. specified using an agreedcoordinate system, specified in relation to a known location), and more.

The LIDAR system may determine the distance between a pair of tangibleobjects based on reflected light. In one embodiment, the LIDAR systemmay process detection results of a sensor which creates temporalinformation indicative of a period of time between the emission of alight signal and the time of its detection by the sensor. The period oftime is occasionally referred to as “time of flight” of the lightsignal. In one example, the light signal may be a short pulse, whoserise and/or fall time may be detected in reception. Using knowninformation about the speed of light in the relevant medium (usuallyair), the information regarding the time of flight of the light signalcan be processed to provide the distance the light signal traveledbetween emission and detection. In another embodiment, the LIDAR systemmay determine the distance based on frequency phase-shift (or multiplefrequency phase-shift). Specifically, the LIDAR system may processinformation indicative of one or more modulation phase shifts (e.g. bysolving some simultaneous equations to give a final measure) of thelight signal. For example, the emitted optical signal may be modulatedwith one or more constant frequencies. The at least one phase shift ofthe modulation between the emitted signal and the detected reflectionmay be indicative of the distance the light traveled between emissionand detection. The modulation may be applied to a continuous wave lightsignal, to a quasi-continuous wave light signal, or to another type ofemitted light signal. It is noted that additional information may beused by the LIDAR system for determining the distance, e.g. locationinformation (e.g. relative positions) between the projection location,the detection location of the signal (especially if distanced from oneanother), and more.

In some embodiments, the LIDAR system may be used for detecting aplurality of objects in an environment of the LIDAR system. The term“detecting an object in an environment of the LIDAR system” broadlyincludes generating information which is indicative of an object thatreflected light toward a detector associated with the LIDAR system. Ifmore than one object is detected by the LIDAR system, the generatedinformation pertaining to different objects may be interconnected, forexample a car is driving on a road, a bird is sitting on the tree, a mantouches a bicycle, a van moves towards a building. The dimensions of theenvironment in which the LIDAR system detects objects may vary withrespect to implementation. For example, the LIDAR system may be used fordetecting a plurality of objects in an environment of a vehicle on whichthe LIDAR system is installed, up to a horizontal distance of 100 m (or200 m, 300 m, etc.), and up to a vertical distance of 10 m (or 25 m, 50m, etc.). In another example, the LIDAR system may be used for detectinga plurality of objects in an environment of a vehicle or within apredefined horizontal range (e.g., 25, 50°, 100°, 180°, etc.), and up toa predefined vertical elevation (e.g., ±10°, ±20°, ±40°-20°, ±90° or0°-90°).

As used herein, the term “detecting an object” may broadly refer todetermining an existence of the object (e.g., an object may exist in acertain direction with respect to the LIDAR system and/or to anotherreference location, or an object may exist in a certain spatial volume).Additionally or alternatively, the term “detecting an object” may referto determining a distance between the object and another location (e.g.a location of the LIDAR system, a location on earth, or a location ofanother object). Additionally or alternatively, the term “detecting anobject” may refer to identifying the object (e.g. classifying a type ofobject such as car, plant, tree, road; recognizing a specific object(e.g., the Washington Monument); determining a license plate number;determining a composition of an object (e.g., solid, liquid,transparent, semitransparent); determining a kinematic parameter of anobject (e.g., whether it is moving, its velocity, its movementdirection, expansion of the object). Additionally or alternatively, theterm “detecting an object” may refer to generating a point cloud map inwhich every point of one or more points of the point cloud mapcorrespond to a location in the object or a location on a face thereof.In one embodiment, the data resolution associated with the point cloudmap representation of the field of view may be associated with 0.1°×0.1°or 0.3°×0.3° of the field of view.

Consistent with the present disclosure, the term “object” broadlyincludes a finite composition of matter that may reflect light from atleast a portion thereof. For example, an object may be at leastpartially solid (e.g. cars, trees); at least partially liquid (e.g.puddles on the road, rain); at least partly gaseous (e.g. fumes,clouds); made from a multitude of distinct particles (e.g. sand storm,fog, spray); and may be of one or more scales of magnitude, such as ˜1millimeter (mm), ˜5 mm, ˜10 mm, ˜50 mm, ˜100 mm, ˜500 mm, ˜1 meter (m),˜5 m, ˜10 m, ˜50 m, ˜100 m, and so on. Smaller or larger objects, aswell as any size in between those examples, may also be detected. It isnoted that for various reasons, the LIDAR system may detect only part ofthe object. For example, in some cases, light may be reflected from onlysome sides of the object (e.g., only the side opposing the LIDAR systemwill be detected); in other cases, light may be projected on only partof the object (e.g. laser beam projected onto a road or a building); inother cases, the object may be partly blocked by another object betweenthe LIDAR system and the detected object; in other cases, the LIDAR'ssensor may only detects light reflected from a portion of the object,e.g., because ambient light or other interferences interfere withdetection of some portions of the object.

Consistent with the present disclosure, a LIDAR system may be configuredto detect objects by scanning the environment of LIDAR system. The term“scanning the environment of LIDAR system” broadly includes illuminatingthe field of view or a portion of the field of view of the LIDAR system.In one example, scanning the environment of LIDAR system may be achievedby moving or pivoting a light deflector to deflect light in differingdirections toward different parts of the field of view. In anotherexample, scanning the environment of LIDAR system may be achieved bychanging a positioning (i.e. location and/or orientation) of a sensorwith respect to the field of view. In another example, scanning theenvironment of LIDAR system may be achieved by changing a positioning(i.e. location and/or orientation) of a light source with respect to thefield of view. In yet another example, scanning the environment of LIDARsystem may be achieved by changing the positions of at least one lightsource and of at least one sensor to move rigidly respect to the fieldof view (i.e. the relative distance and orientation of the at least onesensor and of the at least one light source remains).

As used herein the term “field of view of the LIDAR system” may broadlyinclude an extent of the observable environment of LIDAR system in whichobjects may be detected. It is noted that the field of view (FOV) of theLIDAR system may be affected by various conditions such as but notlimited to: an orientation of the LIDAR system (e.g. is the direction ofan optical axis of the LIDAR system); a position of the LIDAR systemwith respect to the environment (e.g. distance above ground and adjacenttopography and obstacles); operational parameters of the LIDAR system(e.g. emission power, computational settings, defined angles ofoperation), etc. The field of view of LIDAR system may be defined, forexample, by a solid angle (e.g. defined using ϕ, θ angles, in which ϕand θ are angles defined in perpendicular planes, e.g. with respect tosymmetry axes of the LIDAR system and/or its FOV). In one example, thefield of view may also be defined within a certain range (e.g. up to 200m).

Similarly, the term “instantaneous field of view” may broadly include anextent of the observable environment in which objects may be detected bythe LIDAR system at any given moment. For example, for a scanning LIDARsystem, the instantaneous field of view is narrower than the entire FOVof the LIDAR system, and it can be moved within the FOV of the LIDARsystem in order to enable detection in other parts of the FOV of theLIDAR system. The movement of the instantaneous field of view within theFOV of the LIDAR system may be achieved by moving a light deflector ofthe LIDAR system (or external to the LIDAR system), so as to deflectbeams of light to and/or from the LIDAR system in differing directions.In one embodiment, LIDAR system may be configured to scan scene in theenvironment in which the LIDAR system is operating. As used herein theterm “scene” may broadly include some or all of the objects within thefield of view of the LIDAR system, in their relative positions and intheir current states, within an operational duration of the LIDARsystem. For example, the scene may include ground elements (e.g. earth,roads, grass, sidewalks, road surface marking), sky, man-made objects(e.g. vehicles, buildings, signs), vegetation, people, animals, lightprojecting elements (e.g. flashlights, sun, other LIDAR systems), and soon.

Disclosed embodiments may involve obtaining information for use ingenerating reconstructed three-dimensional models. Examples of types ofreconstructed three-dimensional models which may be used include pointcloud models, and Polygon Mesh (e.g. a triangle mesh). The terms “pointcloud” and “point cloud model” are widely known in the art, and shouldbe construed to include a set of data points located spatially in somecoordinate system (i.e., having an identifiable location in a spacedescribed by a respective coordinate system). The term “point cloudpoint” refer to a point in space (which may be dimensionless, or aminiature cellular space, e.g. 1 cm³), and whose location may bedescribed by the point cloud model using a set of coordinates (e.g.(X,Y,Z), (r,ϕ,θ)). By way of example only, the point cloud model maystore additional information for some or all of its points (e.g. colorinformation for points generated from camera images). Likewise, anyother type of reconstructed three-dimensional model may store additionalinformation for some or all of its objects. Similarly, the terms“polygon mesh” and “triangle mesh” are widely known in the art, and areto be construed to include, among other things, a set of vertices, edgesand faces that define the shape of one or more 3D objects (such as apolyhedral object). The faces may include one or more of the following:triangles (triangle mesh), quadrilaterals, or other simple convexpolygons, since this may simplify rendering. The faces may also includemore general concave polygons, or polygons with holes. Polygon meshesmay be represented using differing techniques, such as: Vertex-vertexmeshes, Face-vertex meshes, Winged-edge meshes and Render dynamicmeshes. Different portions of the polygon mesh (e.g., vertex, face,edge) are located spatially in some coordinate system (i.e., having anidentifiable location in a space described by the respective coordinatesystem), either directly and/or relative to one another. The generationof the reconstructed three-dimensional model may be implemented usingany standard, dedicated and/or novel photogrammetry technique, many ofwhich are known in the art. It is noted that other types of models ofthe environment may be generated by the LIDAR system.

Consistent with disclosed embodiments, the LIDAR system may include atleast one projecting unit with a light source configured to projectlight. As used herein the term “light source” broadly refers to anydevice configured to emit light. In one embodiment, the light source maybe a laser such as a solid-state laser, laser diode, a high power laser,or an alternative light source such as, a light emitting diode(LED)-based light source. In addition, light source 112 as illustratedthroughout the figures, may emit light in differing formats, such aslight pulses, continuous wave (CW), quasi-CW, and so on. For example,one type of light source that may be used is a vertical-cavitysurface-emitting laser (VCSEL). Another type of light source that may beused is an external cavity diode laser (ECDL). In some examples, thelight source may include a laser diode configured to emit light at awavelength between about 650 nm and 1,150 nm. Alternatively, the lightsource may include a laser diode configured to emit light at awavelength between about 800 nm and about 1,000 nm, between about 850 nmand about 950 nm, or between about 1,300 nm and about 1,600 nm. Unlessindicated otherwise, the term “about” with regards to a numeric value isdefined as a variance of up to 5% with respect to the stated value.Additional details on the projecting unit and the at least one lightsource are described below with reference to FIGS. 2A-2C.

Consistent with disclosed embodiments, the LIDAR system may include atleast one scanning unit with at least one light deflector configured todeflect light from the light source in order to scan the field of view.The term “light deflector” broadly includes any mechanism or modulewhich is configured to make light deviate from its original path; forexample, a mirror, a prism, controllable lens, a mechanical mirror,mechanical scanning polygons, active diffraction (e.g. controllableLCD), Risley prisms, non-mechanical-electro-optical beam steering (suchas made by Vscent), polarization grating (such as offered by BoulderNon-Linear Systems), optical phased array (OPA), and more. In oneembodiment, a light deflector may include a plurality of opticalcomponents, such as at least one reflecting element (e.g. a mirror), atleast one refracting element (e.g. a prism, a lens), and so on. In oneexample, the light deflector may be movable, to cause light deviate todiffering degrees (e.g. discrete degrees, or over a continuous span ofdegrees). The light deflector may optionally be controllable indifferent ways (e.g. deflect to a degree α, change deflection angle byΔα, move a component of the light deflector by M millimeters, changespeed in which the deflection angle changes). In addition, the lightdeflector may optionally be operable to change an angle of deflectionwithin a single plane (e.g., θ coordinate). The light deflector mayoptionally be operable to change an angle of deflection within twonon-parallel planes (e.g., θ and ϕ coordinates). Alternatively or inaddition, the light deflector may optionally be operable to change anangle of deflection between predetermined settings (e.g. along apredefined scanning route) or otherwise. With respect the use of lightdeflectors in LIDAR systems, it is noted that a light deflector may beused in the outbound direction (also referred to as transmissiondirection, or TX) to deflect light from the light source to at least apart of the field of view. However, a light deflector may also be usedin the inbound direction (also referred to as reception direction, orRX) to deflect light from at least a part of the field of view to one ormore light sensors. Additional details on the scanning unit and the atleast one light deflector are described below with reference to FIGS.3A-3C.

Disclosed embodiments may involve pivoting the light deflector in orderto scan the field of view. As used herein the term “pivoting” broadlyincludes rotating of an object (especially a solid object) about one ormore axis of rotation, while substantially maintaining a center ofrotation fixed. In one embodiment, the pivoting of the light deflectormay include rotation of the light deflector about a fixed axis (e.g., ashaft), but this is not necessarily so. For example, in some MEMS mirrorimplementation, the MEMS mirror may move by actuation of a plurality ofbenders connected to the mirror, the mirror may experience some spatialtranslation in addition to rotation. Nevertheless, such mirror may bedesigned to rotate about a substantially fixed axis, and thereforeconsistent with the present disclosure it considered to be pivoted. Inother embodiments, some types of light deflectors (e.g.non-mechanical-electro-optical beam steering, OPA) do not require anymoving components or internal movements in order to change thedeflection angles of deflected light. It is noted that any discussionrelating to moving or pivoting a light deflector is also mutatismutandis applicable to controlling the light deflector such that itchanges a deflection behavior of the light deflector. For example,controlling the light deflector may cause a change in a deflection angleof beams of light arriving from at least one direction.

Disclosed embodiments may involve receiving reflections associated witha portion of the field of view corresponding to a single instantaneousposition of the light deflector. As used herein, the term “instantaneousposition of the light deflector” (also referred to as “state of thelight deflector”) broadly refers to the location or position in spacewhere at least one controlled component of the light deflector issituated at an instantaneous point in time, or over a short span oftime. In one embodiment, the instantaneous position of light deflectormay be gauged with respect to a frame of reference. The frame ofreference may pertain to at least one fixed point in the LIDAR system.Or, for example, the frame of reference may pertain to at least onefixed point in the scene. In some embodiments, the instantaneousposition of the light deflector may include some movement of one or morecomponents of the light deflector (e.g. mirror, prism), usually to alimited degree with respect to the maximal degree of change during ascanning of the field of view. For example, a scanning of the entire thefield of view of the LIDAR system may include changing deflection oflight over a span of 30° and the instantaneous position of the at leastone light deflector may include angular shifts of the light deflectorwithin 0.05°. In other embodiments, the term “instantaneous position ofthe light deflector” may refer to the positions of the light deflectorduring acquisition of light which is processed to provide data for asingle point of a point cloud (or another type of 3D model) generated bythe LIDAR system. In some embodiments, an instantaneous position of thelight deflector may correspond with a fixed position or orientation inwhich the deflector pauses for a short time during illumination of aparticular sub-region of the LIDAR field of view. In other cases, aninstantaneous position of the light deflector may correspond with acertain position/orientation along a scanned range ofpositions/orientations of the light deflector that the light deflectorpasses through as part of a continuous or semi-continuous scan of theLIDAR field of view. In some embodiments, the light deflector may bemoved such that during a scanning cycle of the LIDAR FOV the lightdeflector is located at a plurality of different instantaneouspositions. In other words, during the period of time in which a scanningcycle occurs, the deflector may be moved through a series of differentinstantaneous positions/orientations, and the deflector may reach eachdifferent instantaneous position/orientation at a different time duringthe scanning cycle.

Consistent with disclosed embodiments, the LIDAR system may include atleast one sensing unit with at least one sensor configured to detectreflections from objects in the field of view. The term “sensor” broadlyincludes any device, element, or system capable of measuring properties(e.g., power, frequency, phase, pulse timing, pulse duration) ofelectromagnetic waves and to generate an output relating to the measuredproperties. In some embodiments, the at least one sensor may include aplurality of detectors constructed from a plurality of detectingelements. The at least one sensor may include light sensors of one ormore types. It is noted that the at least one sensor may includemultiple sensors of the same type which may differ in othercharacteristics (e.g., sensitivity, size). Other types of sensors mayalso be used. Combinations of several types of sensors can be used fordifferent reasons, such as improving detection over a span of ranges(especially in close range); improving the dynamic range of the sensor;improving the temporal response of the sensor; and improving detectionin varying environmental conditions (e.g. atmospheric temperature, rain,etc.).

In one embodiment, the at least one sensor includes a SiPM (Siliconphotomultipliers) which is a solid-state single-photon-sensitive devicebuilt from an array of avalanche photodiode (APD), single photonavalanche diode (SPAD), serving as detection elements on a commonsilicon substrate. In one example, a typical distance between SPADs maybe between about 10 μm and about 50 μm, wherein each SPAD may have arecovery time of between about 20 ns and about 100 ns. Similarphotomultipliers from other, non-silicon materials may also be used.Although a SiPM device works in digital/switching mode, the SiPM is ananalog device because all the microcells may be read in parallel, makingit possible to generate signals within a dynamic range from a singlephoton to hundreds and thousands of photons detected by the differentSPADs. It is noted that outputs from different types of sensors (e.g.,SPAD, APD, SiPM, PIN diode, Photodetector) may be combined together to asingle output which may be processed by a processor of the LIDAR system.Additional details on the sensing unit and the at least one sensor aredescribed below with reference to FIGS. 4A-4C.

Consistent with disclosed embodiments, the LIDAR system may include orcommunicate with at least one processor configured to execute differingfunctions. The at least one processor may constitute any physical devicehaving an electric circuit that performs a logic operation on input orinputs. For example, the at least one processor may include one or moreintegrated circuits (IC), including Application-specific integratedcircuit (ASIC), microchips, microcontrollers, microprocessors, all orpart of a central processing unit (CPU), graphics processing unit (GPU),digital signal processor (DSP), field-programmable gate array (FPGA), orother circuits suitable for executing instructions or performing logicoperations. The instructions executed by at least one processor may, forexample, be pre-loaded into a memory integrated with or embedded intothe controller or may be stored in a separate memory. The memory maycomprise a Random Access Memory (RAM), a Read-Only Memory (ROM), a harddisk, an optical disk, a magnetic medium, a flash memory, otherpermanent, fixed, or volatile memory, or any other mechanism capable ofstoring instructions. In some embodiments, the memory is configured tostore information representative data about objects in the environmentof the LIDAR system. In some embodiments, the at least one processor mayinclude more than one processor. Each processor may have a similarconstruction or the processors may be of differing constructions thatare electrically connected or disconnected from each other. For example,the processors may be separate circuits or integrated in a singlecircuit. When more than one processor is used, the processors may beconfigured to operate independently or collaboratively. The processorsmay be coupled electrically, magnetically, optically, acoustically,mechanically or by other means that permit them to interact. Additionaldetails on the processing unit and the at least one processor aredescribed below with reference to FIGS. 5A-5C.

System Overview

FIG. 1A illustrates a LIDAR system 100 including a projecting unit 102,a scanning unit 104, a sensing unit 106, and a processing unit 108.LIDAR system 100 may be mountable on a vehicle 110. Consistent withembodiments of the present disclosure, projecting unit 102 may includeat least one light source 112, scanning unit 104 may include at leastone light deflector 114, sensing unit 106 may include at least onesensor 116, and processing unit 108 may include at least one processor118. In one embodiment, at least one processor 118 may be configured tocoordinate operation of the at least one light source 112 with themovement of at least one light deflector 114 in order to scan a field ofview 120. During a scanning cycle, each instantaneous position of atleast one light deflector 114 may be associated with a particularportion 122 of field of view 120. In addition, LIDAR system 100 mayinclude at least one optional optical window 124 for directing lightprojected towards field of view 120 and/or receiving light reflectedfrom objects in field of view 120. Optional optical window 124 may servedifferent purposes, such as collimation of the projected light andfocusing of the reflected light. In one embodiment, optional opticalwindow 124 may be an opening, a flat window, a lens, or any other typeof optical window.

Consistent with the present disclosure, LIDAR system 100 may be used inautonomous or semi-autonomous road-vehicles (for example, cars, buses,vans, trucks and any other terrestrial vehicle). Autonomousroad-vehicles with LIDAR system 100 may scan their environment and driveto a destination vehicle without human input. Similarly, LIDAR system100 may also be used in autonomous/semi-autonomous aerial-vehicles (forexample, UAV, drones, quadcopters, and any other airborne vehicle ordevice); or in an autonomous or semi-autonomous water vessel (e.g.,boat, ship, submarine, or any other watercraft). Autonomousaerial-vehicles and water craft with LIDAR system 100 may scan theirenvironment and navigate to a destination autonomously or using a remotehuman operator. According to one embodiment, vehicle 110 (either aroad-vehicle, aerial-vehicle, or watercraft) may use LIDAR system 100 toaid in detecting and scanning the environment in which vehicle 110 isoperating.

In some embodiments, LIDAR system 100 may include one or more scanningunits 104 to scan the environment around vehicle 110. LIDAR system 100may be attached or mounted to any part of vehicle 110. Sensing unit 106may receive reflections from the surroundings of vehicle 110, andtransfer reflections signals indicative of light reflected from objectsin field of view 120 to processing unit 108. Consistent with the presentdisclosure, scanning units 104 may be mounted to or incorporated into abumper, a fender, a side panel, a spoiler, a roof, a headlight assembly,a taillight assembly, a rear-view mirror assembly, a hood, a trunk orany other suitable part of vehicle 110 capable of housing at least aportion of the LIDAR system. In some cases, LIDAR system 100 may capturea complete surround view of the environment of vehicle 110. Thus, LIDARsystem 100 may have a 360-degree horizontal field of view. In oneexample, as shown in FIG. 1A, LIDAR system 100 may include a singlescanning unit 104 mounted on a roof vehicle 110. Alternatively, LIDARsystem 100 may include multiple scanning units (e.g., two, three, four,or more scanning units 104) each with a field of few such that in theaggregate the horizontal field of view is covered by a 360-degree scanaround vehicle 110. One skilled in the art will appreciate that LIDARsystem 100 may include any number of scanning units 104 arranged in anymanner, each with an 80° to 120° field of view or less, depending on thenumber of units employed. Moreover, a 360-degree horizontal field ofview may be also obtained by mounting a multiple LIDAR systems 100 onvehicle 110, each with a single scanning unit 104. It is neverthelessnoted, that the one or more LIDAR systems 100 do not have to provide acomplete 360° field of view, and that narrower fields of view may beuseful in some situations. For example, vehicle 110 may require a firstLIDAR system 100 having an field of view of 75° looking ahead of thevehicle, and possibly a second LIDAR system 100 with a similar FOVlooking backward (optionally with a lower detection range). It is alsonoted that different vertical field of view angles may also beimplemented.

FIG. 1B is an image showing an exemplary output from a single scanningcycle of LIDAR system 100 mounted on vehicle 110 consistent withdisclosed embodiments. In this example, scanning unit 104 isincorporated into a right headlight assembly of vehicle 110. Every graydot in the image corresponds to a location in the environment aroundvehicle 110 determined from reflections detected by sensing unit 106. Inaddition to location, each gray dot may also be associated withdifferent types of information, for example, intensity (e.g., how muchlight returns back from that location), reflectivity, proximity to otherdots, and more. In one embodiment, LIDAR system 100 may generate aplurality of point-cloud data entries from detected reflections ofmultiple scanning cycles of the field of view to enable, for example,determining a point cloud model of the environment around vehicle 110.

FIG. 1C is an image showing a representation of the point cloud modeldetermined from the output of LIDAR system 100. Consistent withdisclosed embodiments, by processing the generated point-cloud dataentries of the environment around vehicle 110, a surround-view image maybe produced from the point cloud model. In one embodiment, the pointcloud model may be provided to a feature extraction module, whichprocesses the point cloud information to identify a plurality offeatures. Each feature may include data about different aspects of thepoint cloud and/or of objects in the environment around vehicle 110(e.g. cars, trees, people, and roads). Features may have the sameresolution of the point cloud model (i.e. having the same number of datapoints, optionally arranged into similar sized 2D arrays), or may havedifferent resolutions. The features may be stored in any kind of datastructure (e.g. raster, vector, 2D array, 1D array). In addition,virtual features, such as a representation of vehicle 110, border lines,or bounding boxes separating regions or objects in the image (e.g., asdepicted in FIG. 1B), and icons representing one or more identifiedobjects, may be overlaid on the representation of the point cloud modelto form the final surround-view image. For example, a symbol of vehicle110 may be overlaid at a center of the surround-view image.

The Projecting Unit

FIGS. 2A-2D depict various configurations of projecting unit 102 and itsrole in LIDAR system 100. Specifically, FIG. 2A is a diagramillustrating projecting unit 102 with a single light source, FIG. 2B isa diagram illustrating a plurality of projecting units 102 with aplurality of light sources aimed at a common light deflector 114, FIG.2C is a diagram illustrating projecting unit 102 with a primary and asecondary light sources 112, and FIG. 2D is a diagram illustrating anasymmetrical deflector used in some configurations of projecting unit102. One skilled in the art will appreciate that the depictedconfigurations of projecting unit 102 may have numerous variations andmodifications.

FIG. 2A illustrates an example of a bi-static configuration of LIDARsystem 100 in which projecting unit 102 includes a single light source112. The term “bi-static configuration” broadly refers to LIDAR systemsconfigurations in which the projected light exiting the LIDAR system andthe reflected light entering the LIDAR system pass through differentoptical channels. Specifically, the outbound light radiation may passthrough a first optical window (not shown) and the inbound lightradiation may pass through another optical window (not shown). In theexample depicted in FIG. 2A, the Bi-static configuration includes aconfiguration where scanning unit 104 includes two light deflectors, afirst light deflector 114A for outbound light and a second lightdeflector 114B for inbound light (the inbound light in LIDAR systemincludes emitted light reflected from objects in the scene, and may alsoinclude ambient light arriving from other sources). In such aconfiguration the inbound and outbound paths differ.

In this embodiment, all the components of LIDAR system 100 may becontained within a single housing 200, or may be divided among aplurality of housings. As shown, projecting unit 102 is associated witha single light source 112 that includes a laser diode 202A (or one ormore laser diodes coupled together) configured to emit light (projectedlight 204). In one non limiting example, the light projected by lightsource 112 may be at a wavelength between about 800 nm and 950 nm, havean average power between about 50 mW and about 500 mW, have a peak powerbetween about 50 W and about 200 W, and a pulse width of between about 2ns and about 100 ns. In addition, light source 112 may optionally beassociated with optical assembly 202B used for manipulation of the lightemitted by laser diode 202A (e.g. for collimation, focusing, etc.). Itis noted that other types of light sources 112 may be used, and that thedisclosure is not restricted to laser diodes. In addition, light source112 may emit its light in different formats, such as light pulses,frequency modulated, continuous wave (CW), quasi-CW, or any other formcorresponding to the particular light source employed. The projectionformat and other parameters may be changed by the light source from timeto time based on different factors, such as instructions from processingunit 108. The projected light is projected towards an outbound deflector114A that functions as a steering element for directing the projectedlight in field of view 120. In this example, scanning unit 104 alsoinclude a pivotable return deflector 114B that direct photons (reflectedlight 206) reflected back from an object 208 within field of view 120toward sensor 116. The reflected light is detected by sensor 116 andinformation about the object (e.g., the distance to object 212) isdetermined by processing unit 108.

In this figure, LIDAR system 100 is connected to a host 210. Consistentwith the present disclosure, the term “host” refers to any computingenvironment that may interface with LIDAR system 100, it may be avehicle system (e.g., part of vehicle 110), a testing system, a securitysystem, a surveillance system, a traffic control system, an urbanmodelling system, or any system that monitors its surroundings. Suchcomputing environment may include at least one processor and/or may beconnected LIDAR system 100 via the cloud. In some embodiments, host 210may also include interfaces to external devices such as camera andsensors configured to measure different characteristics of host 210(e.g., acceleration, steering wheel deflection, reverse drive, etc.).Consistent with the present disclosure, LIDAR system 100 may be fixed toa stationary object associated with host 210 (e.g. a building, a tripod)or to a portable system associated with host 210 (e.g., a portablecomputer, a movie camera). Consistent with the present disclosure, LIDARsystem 100 may be connected to host 210, to provide outputs of LIDARsystem 100 (e.g., a 3D model, a reflectivity image) to host 210.Specifically, host 210 may use LIDAR system 100 to aid in detecting andscanning the environment of host 210 or any other environment. Inaddition, host 210 may integrate, synchronize or otherwise use togetherthe outputs of LIDAR system 100 with outputs of other sensing systems(e.g. cameras, microphones, radar systems). In one example, LIDAR system100 may be used by a security system. This embodiment is described ingreater detail below with reference to FIG. 7.

LIDAR system 100 may also include a bus 212 (or other communicationmechanisms) that interconnect subsystems and components for transferringinformation within LIDAR system 100. Optionally, bus 212 (or anothercommunication mechanism) may be used for interconnecting LIDAR system100 with host 210. In the example of FIG. 2A, processing unit 108includes two processors 118 to regulate the operation of projecting unit102, scanning unit 104, and sensing unit 106 in a coordinated mannerbased, at least partially, on information received from internalfeedback of LIDAR system 100. In other words, processing unit 108 may beconfigured to dynamically operate LIDAR system 100 in a closed loop. Aclosed loop system is characterized by having feedback from at least oneof the elements and updating one or more parameters based on thereceived feedback. Moreover, a closed loop system may receive feedbackand update its own operation, at least partially, based on thatfeedback. A dynamic system or element is one that may be updated duringoperation.

According to some embodiments, scanning the environment around LIDARsystem 100 may include illuminating field of view 120 with light pulses.The light pulses may have parameters such as: pulse duration, pulseangular dispersion, wavelength, instantaneous power, photon density atdifferent distances from light source 112, average power, pulse powerintensity, pulse width, pulse repetition rate, pulse sequence, pulseduty cycle, wavelength, phase, polarization, and more. Scanning theenvironment around LIDAR system 100 may also include detecting andcharacterizing various aspects of the reflected light. Characteristicsof the reflected light may include, for example: time-of-flight (i.e.,time from emission until detection), instantaneous power (e.g., powersignature), average power across entire return pulse, and photondistribution/signal over return pulse period. By comparingcharacteristics of a light pulse with characteristics of correspondingreflections, a distance and possibly a physical characteristic, such asreflected intensity of object 212 may be estimated. By repeating thisprocess across multiple adjacent portions 122, in a predefined pattern(e.g., raster, Lissajous or other patterns) an entire scan of field ofview 120 may be achieved. As discussed below in greater detail, in somesituations LIDAR system 100 may direct light to only some of theportions 122 in field of view 120 at every scanning cycle. Theseportions may be adjacent to each other, but not necessarily so.

In another embodiment, LIDAR system 100 may include network interface214 for communicating with host 210 (e.g., a vehicle controller). Thecommunication between LIDAR system 100 and host 210 is represented by adashed arrow. In one embodiment, network interface 214 may include anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, networkinterface 214 may include a local area network (LAN) card to provide adata communication connection to a compatible LAN. In anotherembodiment, network interface 214 may include an Ethernet port connectedto radio frequency receivers and transmitters and/or optical (e.g.,infrared) receivers and transmitters. The specific design andimplementation of network interface 214 depends on the communicationsnetwork(s) over which LIDAR system 100 and host 210 are intended tooperate. For example, network interface 214 may be used, for example, toprovide outputs of LIDAR system 100 to the external system, such as a 3Dmodel, operational parameters of LIDAR system 100, and so on. In otherembodiment, the communication unit may be used, for example, to receiveinstructions from the external system, to receive information regardingthe inspected environment, to receive information from another sensor,etc.

FIG. 2B illustrates an example of a monostatic configuration of LIDARsystem 100 including a plurality projecting units 102. The term“monostatic configuration” broadly refers to LIDAR systemsconfigurations in which the projected light exiting from the LIDARsystem and the reflected light entering the LIDAR system pass through atleast a partially shared optical path. In one example, the outboundlight beam and the inbound light beam may share at least one opticalassembly through which both light beams. In another example, theoutbound light radiation may pass through an optical window (not shown)and the inbound light radiation may pass through the same opticalwindow. A monostatic configuration may include a configuration where thescanning unit 104 includes a single light deflector 114 that directs theprojected light towards field of view 120 and directs the reflectedlight towards a sensor 116. As shown, both projected light 204 andreflected light 206 hits an asymmetrical deflector 216. The term“asymmetrical deflector” refers to any optical device having two sidescapable of deflecting a beam of light hitting it from one side in adifferent direction than it deflects a beam of light hitting it from thesecond side. In one example, the asymmetrical deflector does not deflectprojected light 204 and deflects reflected light 206 towards sensor 116.One example of an asymmetrical deflector may include a polarization beamsplitter. In another example, asymmetrical 216 may include an opticalisolator that allows the passage of light in only one direction.Consistent with the present disclosure, a monostatic configuration ofLIDAR system 100 may include an asymmetrical deflector to preventreflected light from hitting light source 112, and to direct all thereflected light toward sensor 116, thereby increasing detectionsensitivity.

In the embodiment of FIG. 2B, LIDAR system 100 includes three projectingunits 102 each with a single of light source 112 aimed at a common lightdeflector 114. In one embodiment, the plurality of light sources 112(including two or more light sources) may project light withsubstantially the same wavelength and each light source 112 is generallyassociated with a differing area of the field of view (denoted in thefigure as 120A, 120B, and 120C). This enables scanning of a broaderfield of view than can be achieved with a light source 112. In anotherembodiment, the plurality of light sources 102 may project light withdiffering wavelengths, and all the light sources 112 may be directed tothe same portion (or overlapping portions) of field of view 120.

FIG. 2C illustrates an example of LIDAR system 100 in which projectingunit 102 includes a primary light source 112A and a secondary lightsource 112B. Primary light source 112A may project light with a longerwavelength than is sensitive to the human eye in order to optimize SNRand detection range. For example, primary light source 112A may projectlight with a wavelength between about 750 nm and 1,100 nm. In contrast,secondary light source 112B may project light with a wavelength visibleto the human eye. For example, secondary light source 112B may projectlight with a wavelength between about 400 nm and 700 nm. In oneembodiment, secondary light source 112B may project light alongsubstantially the same optical path the as light projected by primarylight source 112A. Both light sources may be time-synchronized and mayproject light emission together or in interleaved pattern. An interleavepattern means that the light sources are not active at the same timewhich may mitigate mutual interference. A person who is of skill in theart would readily see that other combinations of wavelength ranges andactivation schedules may also be implemented.

Consistent with some embodiments, secondary light source 112B may causehuman eyes to blink when it is too close to the LIDAR optical outputport. This may ensure an eye safety mechanism not feasible with typicallaser sources that utilize the near-infrared light spectrum. In anotherembodiment, secondary light source 112B may be used for calibration andreliability at a point of service, in a manner somewhat similar to thecalibration of headlights with a special reflector/pattern at a certainheight from the ground with respect to vehicle 110. An operator at apoint of service could examine the calibration of the LIDAR by simplevisual inspection of the scanned pattern over a featured target such atest pattern board at a designated distance from LIDAR system 100. Inaddition, secondary light source 112B may provide means for operationalconfidence that the LIDAR is working for the end-user. For example, thesystem may be configured to permit a human to place a hand in front oflight deflector 114 to test its operation.

Secondary light source 112B may also have a non-visible element that candouble as a backup system in case primary light source 112A fails. Thisfeature may be useful for fail-safe devices with elevated functionalsafety ratings. Given that secondary light source 112B may be visibleand also due to reasons of cost and complexity, secondary light source112B may be associated with a smaller power compared to primary lightsource 112A. Therefore, in case of a failure of primary light source112A, the system functionality will fall back to secondary light source112B set of functionalities and capabilities. While the capabilities ofsecondary light source 112B may be inferior to the capabilities ofprimary light source 12A, LIDAR system 100 system may be designed insuch a fashion to enable vehicle 110 to safely arrive its destination.

FIG. 2D illustrates asymmetrical deflector 216 that may be part of LIDARsystem 100. In the illustrated example, asymmetrical deflector 216includes a reflective surface 218 (such as a mirror) and a one-waydeflector 220. While not necessarily so, asymmetrical deflector 216 mayoptionally be a static deflector. Asymmetrical deflector 216 may be usedin a monostatic configuration of LIDAR system 100, in order to allow acommon optical path for transmission and for reception of light via theat least one deflector 114, e.g. as illustrated in FIGS. 2B and 2C.However, typical asymmetrical deflectors such as beam splitters arecharacterized by energy losses, especially in the reception path, whichmay be more sensitive to power loses than the transmission path.

As depicted in FIG. 2D, LIDAR system 100 may include asymmetricaldeflector 216 positioned in the transmission path, which includesone-way deflector 220 for separating between the transmitted andreceived light signals. Optionally, one-way deflector 220 may besubstantially transparent to the transmission light and substantiallyreflective to the received light. The transmitted light is generated byprojecting unit 102 and may travel through one-way deflector 220 toscanning unit 104 which deflects it towards the optical outlet. Thereceived light arrives through the optical inlet, to the at least onedeflecting element 114, which deflects the reflections signal into aseparate path away from the light source and towards sensing unit 106.Optionally, asymmetrical deflector 216 may be combined with a polarizedlight source 112 which is linearly polarized with the same polarizationaxis as one-way deflector 220. Notably, the cross-section of theoutbound light beam is much smaller than that of the reflectionssignals. Accordingly, LIDAR system 100 may include one or more opticalcomponents (e.g. lens, collimator) for focusing or otherwisemanipulating the emitted polarized light beam to the dimensions of theasymmetrical deflector 216. In one embodiment, one-way deflector 220 maybe a polarizing beam splitter that is virtually transparent to thepolarized light beam.

Consistent with some embodiments, LIDAR system 100 may further includeoptics 222 (e.g., a quarter wave plate retarder) for modifying apolarization of the emitted light. For example, optics 222 may modify alinear polarization of the emitted light beam to circular polarization.Light reflected back to system 100 from the field of view would arriveback through deflector 114 to optics 222, bearing a circularpolarization with a reversed handedness with respect to the transmittedlight. Optics 222 would then convert the received reversed handednesspolarization light to a linear polarization that is not on the same axisas that of the polarized beam splitter 216. As noted above, the receivedlight-patch is larger than the transmitted light-patch, due to opticaldispersion of the beam traversing through the distance to the target.

Some of the received light will impinge on one-way deflector 220 thatwill reflect the light towards sensor 106 with some power loss. However,another part of the received patch of light will fall on a reflectivesurface 218 which surrounds one-way deflector 220 (e.g., polarizing beamsplitter slit). Reflective surface 218 will reflect the light towardssensing unit 106 with substantially zero power loss. One-way deflector220 would reflect light that is composed of various polarization axesand directions that will eventually arrive at the detector. Optionally,sensing unit 106 may include sensor 116 that is agnostic to the laserpolarization, and is primarily sensitive to the amount of impingingphotons at a certain wavelength range.

It is noted that the proposed asymmetrical deflector 216 provides farsuperior performances when compared to a simple mirror with a passagehole in it. In a mirror with a hole, all of the reflected light whichreaches the hole is lost to the detector. However, in deflector 216,one-way deflector 220 deflects a significant portion of that light(e.g., about 50%) toward the respective sensor 116. In LIDAR systems,the number photons reaching the LIDAR from remote distances is verylimited, and therefore the improvement in photon capture rate isimportant.

According to some embodiments, a device for beam splitting and steeringis described. A polarized beam may be emitted from a light source havinga first polarization. The emitted beam may be directed to pass through apolarized beam splitter assembly. The polarized beam splitter assemblyincludes on a first side a one-directional slit and on an opposing sidea mirror. The one-directional slit enables the polarized emitted beam totravel toward a quarter-wave-plate/wave-retarder which changes theemitted signal from a polarized signal to a linear signal (or viceversa) so that subsequently reflected beams cannot travel through theone-directional slit.

The Scanning Unit

FIGS. 3A-3D depict various configurations of scanning unit 104 and itsrole in LIDAR system 100. Specifically, FIG. 3A is a diagramillustrating scanning unit 104 with a MEMS mirror (e.g., square shaped),FIG. 3B is a diagram illustrating another scanning unit 104 with a MEMSmirror (e.g., round shaped), FIG. 3C is a diagram illustrating scanningunit 104 with an array of reflectors used for monostatic scanning LIDARsystem, and FIG. 3D is a diagram illustrating an example LIDAR system100 that mechanically scans the environment around LIDAR system 100. Oneskilled in the art will appreciate that the depicted configurations ofscanning unit 104 are exemplary only, and may have numerous variationsand modifications within the scope of this disclosure.

FIG. 3A illustrates an example scanning unit 104 with a single axissquare MEMS mirror 300. In this example MEMS mirror 300 functions as atleast one deflector 114. As shown, scanning unit 104 may include one ormore actuators 302 (specifically, 302A and 302B). In one embodiment,actuator 302 may be made of semiconductor (e.g., silicon) and includes apiezoelectric layer (e.g. PZT, Lead zirconate titanate, aluminumnitride), which changes its dimension in response to electric signalsapplied by an actuation controller, a semi conductive layer, and a baselayer. In one embodiment, the physical properties of actuator 302 maydetermine the mechanical stresses that actuator 302 experiences whenelectrical current passes through it. When the piezoelectric material isactivated it exerts force on actuator 302 and causes it to bend. In oneembodiment, the resistivity of one or more actuators 302 may be measuredin an active state (Ractive) when mirror 300 is deflected at a certainangular position and compared to the resistivity at a resting state(Rrest). Feedback including Ractive may provide information to determinethe actual mirror deflection angle compared to an expected angle, and,if needed, mirror 300 deflection may be corrected. The differencebetween Rrest and Ractive may be correlated by a mirror drive into anangular deflection value that may serve to close the loop. Thisembodiment may be used for dynamic tracking of the actual mirrorposition and may optimize response, amplitude, deflection efficiency,and frequency for both linear mode and resonant mode MEMS mirrorschemes. This embodiment is described in greater detail below withreference to FIGS. 32-34.

During scanning, current (represented in the figure as the dashed line)may flow from contact 304A to contact 304B (through actuator 302A,spring 306A, mirror 300, spring 306B, and actuator 302B). Isolation gapsin semiconducting frame 308 such as isolation gap 310 may cause actuator302A and 302B to be two separate islands connected electrically throughsprings 306 and frame 308. The current flow, or any associatedelectrical parameter (voltage, current frequency, capacitance, relativedielectric constant, etc.), may be monitored by an associated positionfeedback. In case of a mechanical failure—where one of the components isdamaged—the current flow through the structure would alter and changefrom its functional calibrated values. At an extreme situation (forexample, when a spring is broken), the current would stop completely dueto a circuit break in the electrical chain by means of a faulty element.

FIG. 3B illustrates another example scanning unit 104 with a dual axisround MEMS mirror 300. In this example MEMS mirror 300 functions as atleast one deflector 114. In one embodiment, MEMS mirror 300 may have adiameter of between about 1 mm to about 5 mm. As shown, scanning unit104 may include four actuators 302 (302A, 302B, 302C, and 302D) each maybe at a differing length. In the illustrated example, the current(represented in the figure as the dashed line) flows from contact 304Ato contact 304D, but in other cases current may flow from contact 304Ato contact 304B, from contact 304A to contact 304C, from contact 304B tocontact 304C, from contact 304B to contact 304D, or from contact 304C tocontact 304D. Consistent with some embodiments, a dual axis MEMS mirrormay be configured to deflect light in a horizontal direction and in avertical direction. For example, the angles of deflection of a dual axisMEMS mirror may be between about 0° to 30° in the vertical direction andbetween about 0° to 50° in the horizontal direction. One skilled in theart will appreciate that the depicted configuration of mirror 300 mayhave numerous variations and modifications. In one example, at least ofdeflector 114 may have a dual axis square-shaped mirror or single axisround-shaped mirror. Examples of round and square mirror are depicted inFIGS. 3A and 3B as examples only. Any shape may be employed depending onsystem specifications. In one embodiment, actuators 302 may beincorporated as an integral part of at least of deflector 114, such thatpower to move MEMS mirror 300 is applied directly towards it. Inaddition, MEMS mirror 300 maybe connected to frame 308 by one or morerigid supporting elements. In another embodiment, at least of deflector114 may include an electrostatic or electromagnetic MEMS mirror.

As described above, a monostatic scanning LIDAR system utilizes at leasta portion of the same optical path for emitting projected light 204 andfor receiving reflected light 206. The light beam in the outbound pathmay be collimated and focused into a narrow beam while the reflectionsin the return path spread into a larger patch of light, due todispersion. In one embodiment, scanning unit 104 may have a largereflection area in the return path and asymmetrical deflector 216 thatredirects the reflections (i.e., reflected light 206) to sensor 116. Inone embodiment, scanning unit 104 may include a MEMS mirror with a largereflection area and negligible impact on the field of view and the framerate performance. Additional details about the asymmetrical deflector216 are provided below with reference to FIG. 2D.

In some embodiments (e.g. as exemplified in FIG. 3C), scanning unit 104may include a deflector array (e.g. a reflector array) with small lightdeflectors (e.g. mirrors). In one embodiment, implementing lightdeflector 114 as a group of smaller individual light deflectors workingin synchronization may allow light deflector 114 to perform at a highscan rate with larger angles of deflection. The deflector array mayessentially act as a large light deflector (e.g. a large mirror) interms of effective area. The deflector array may be operated using ashared steering assembly configuration that allows sensor 116 to collectreflected photons from substantially the same portion of field of view120 being concurrently illuminated by light source 112. The term“concurrently” means that the two selected functions occur duringcoincident or overlapping time periods, either where one begins and endsduring the duration of the other, or where a later one starts before thecompletion of the other.

FIG. 3C illustrates an example of scanning unit 104 with a reflectorarray 312 having small mirrors. In this embodiment, reflector array 312functions as at least one deflector 114. Reflector array 312 may includea plurality of reflector units 314 configured to pivot (individually ortogether) and steer light pulses toward field of view 120. For example,reflector array 312 may be a part of an outbound path of light projectedfrom light source 112. Specifically, reflector array 312 may directprojected light 204 towards a portion of field of view 120. Reflectorarray 312 may also be part of a return path for light reflected from asurface of an object located within an illumined portion of field ofview 120. Specifically, reflector array 312 may direct reflected light206 towards sensor 116 or towards asymmetrical deflector 216. In oneexample, the area of reflector array 312 may be between about 75 toabout 150 mm², where each reflector units 314 may have a width of about10 μm and the supporting structure may be lower than 100 μm.

According to some embodiments, reflector array 312 may include one ormore sub-groups of steerable deflectors. Each sub-group of electricallysteerable deflectors may include one or more deflector units, such asreflector unit 314. For example, each steerable deflector unit 314 mayinclude at least one of a MEMS mirror, a reflective surface assembly,and an electromechanical actuator. In one embodiment, each reflectorunit 314 may be individually controlled by an individual processor (notshown), such that it may tilt towards a specific angle along each of oneor two separate axes. Alternatively, reflector array 312 may beassociated with a common controller (e.g., processor 118) configured tosynchronously manage the movement of reflector units 314 such that atleast part of them will pivot concurrently and point in approximatelythe same direction.

In addition, at least one processor 118 may select at least onereflector unit 314 for the outbound path (referred to hereinafter as “TXMirror”) and a group of reflector units 314 for the return path(referred to hereinafter as “RX Mirror”). Consistent with the presentdisclosure, increasing the number of TX Mirrors may increase a reflectedphotons beam spread. Additionally, decreasing the number of RX Mirrorsmay narrow the reception field and compensate for ambient lightconditions (such as clouds, rain, fog, extreme heat, and otherenvironmental conditions) and improve the signal to noise ratio. Also,as indicated above, the emitted light beam is typically narrower thanthe patch of reflected light, and therefore can be fully deflected by asmall portion of the deflection array. Moreover, it is possible to blocklight reflected from the portion of the deflection array used fortransmission (e.g. the TX mirror) from reaching sensor 116, therebyreducing an effect of internal reflections of the LIDAR system 100 onsystem operation. In addition, at least one processor 118 may pivot oneor more reflector units 314 to overcome mechanical impairments anddrifts due, for example, to thermal and gain effects. In an example, oneor more reflector units 314 may move differently than intended(frequency, rate, speed etc.) and their movement may be compensated forby electrically controlling the deflectors appropriately.

FIG. 3D illustrates an exemplary LIDAR system 100 that mechanicallyscans the environment of LIDAR system 100. In this example, LIDAR system100 may include a motor or other mechanisms for rotating housing 200about the axis of the LIDAR system 100. Alternatively, the motor (orother mechanism) may mechanically rotate a rigid structure of LIDARsystem 100 on which one or more light sources 112 and one or moresensors 116 are installed, thereby scanning the environment. Asdescribed above, projecting unit 102 may include at least one lightsource 112 configured to project light emission. The projected lightemission may travel along an outbound path towards field of view 120.Specifically, the projected light emission may be reflected by deflector114A through an exit aperture 314 when projected light 204 traveltowards optional optical window 124. The reflected light emission maytravel along a return path from object 208 towards sensing unit 106. Forexample, the reflected light 206 may be reflected by deflector 114B whenreflected light 206 travels towards sensing unit 106. A person skilledin the art would appreciate that a LIDAR system with a rotationmechanism for synchronically rotating one or more light sources or oneor more sensors, may use this synchronized rotation instead of (or inaddition to) steering an internal light deflector.

In embodiments in which the scanning of field of view 120 is mechanical,the projected light emission may be directed to exit aperture 314 thatis part of a wall 316 separating projecting unit 102 from other parts ofLIDAR system 100. In some examples, wall 316 can be formed from atransparent material (e.g., glass) coated with a reflective material toform deflector 114B. In this example, exit aperture 314 may correspondto the portion of wall 316 that is not coated by the reflectivematerial. Additionally or alternatively, exit aperture 314 may include ahole or cut-away in the wall 316. Reflected light 206 may be reflectedby deflector 114B and directed towards an entrance aperture 318 ofsensing unit 106. In some examples, an entrance aperture 318 may includea filtering window configured to allow wavelengths in a certainwavelength range to enter sensing unit 106 and attenuate otherwavelengths. The reflections of object 208 from field of view 120 may bereflected by deflector 114B and hit sensor 116. By comparing severalproperties of reflected light 206 with projected light 204, at least oneaspect of object 208 may be determined. For example, by comparing a timewhen projected light 204 was emitted by light source 112 and a time whensensor 116 received reflected light 206, a distance between object 208and LIDAR system 100 may be determined. In some examples, other aspectsof object 208, such as shape, color, material, etc. may also bedetermined.

In some examples, the LIDAR system 100 (or part thereof, including atleast one light source 112 and at least one sensor 116) may be rotatedabout at least one axis to determine a three-dimensional map of thesurroundings of the LIDAR system 100. For example, the LIDAR system 100may be rotated about a substantially vertical axis as illustrated byarrow 320 in order to scan field of 120. Although FIG. 3D illustratesthat the LIDAR system 100 is rotated clock-wise about the axis asillustrated by the arrow 320, additionally or alternatively, the LIDARsystem 100 may be rotated in a counter clockwise direction. In someexamples, the LIDAR system 100 may be rotated 360 degrees about thevertical axis. In other examples, the LIDAR system 100 may be rotatedback and forth along a sector smaller than 360-degree of the LIDARsystem 100. For example, the LIDAR system 100 may be mounted on aplatform that wobbles back and forth about the axis without making acomplete rotation.

The Sensing Unit

FIGS. 4A-4E depict various configurations of sensing unit 106 and itsrole in LIDAR system 100. Specifically. FIG. 4A is a diagramillustrating an example sensing unit 106 with a detector array, FIG. 4Bis a diagram illustrating monostatic scanning using a two-dimensionalsensor, FIG. 4C is a diagram illustrating an example of atwo-dimensional sensor 116, FIG. 4D is a diagram illustrating a lensarray associated with sensor 116, and FIG. 4E includes three diagramillustrating the lens structure. One skilled in the art will appreciatethat the depicted configurations of sensing unit 106 are exemplary onlyand may have numerous alternative variations and modificationsconsistent with the principles of this disclosure.

FIG. 4A illustrates an example of sensing unit 106 with detector array400. In this example, at least one sensor 116 includes detector array400. LIDAR system 100 is configured to detect objects (e.g., bicycle208A and cloud 208B) in field of view 120 located at different distancesfrom LIDAR system 100 (could be meters or more). Objects 208 may be asolid object (e.g. a road, a tree, a car, a person), fluid object (e.g.fog, water, atmosphere particles), or object of another type (e.g. dustor a powdery illuminated object). When the photons emitted from lightsource 112 hit object 208 they either reflect, refract, or get absorbed.Typically, as shown in the figure, only a portion of the photonsreflected from object 208A enters optional optical window 124. As each˜15 cm change in distance results in a travel time difference of 1 ns(since the photons travel at the speed of light to and from object 208),the time differences between the travel times of different photonshitting the different objects may be detectable by a time-of-flightsensor with sufficiently quick response.

Sensor 116 includes a plurality of detection elements 402 for detectingphotons of a photonic pulse reflected back from field of view 120. Thedetection elements may all be included in detector array 400, which mayhave a rectangular arrangement (e.g. as shown) or any other arrangement.Detection elements 402 may operate concurrently or partiallyconcurrently with each other. Specifically, each detection element 402may issue detection information for every sampling duration (e.g. every1 nanosecond). In one example, detector array 400 may be a SiPM (Siliconphotomultipliers) which is a solid-state single-photon-sensitive devicebuilt from an array of single photon avalanche diode (, SPAD, serving asdetection elements 402) on a common silicon substrate. Similarphotomultipliers from other, non-silicon materials may also be used.Although a SiPM device works in digital/switching mode, the SiPM is ananalog device because all the microcells are read in parallel, making itpossible to generate signals within a dynamic range from a single photonto hundreds and thousands of photons detected by the different SPADs. Asmentioned above, more than one type of sensor may be implemented (e.g.SiPM and APD). Possibly, sensing unit 106 may include at least one APDintegrated into an SiPM array and/or at least one APD detector locatednext to a SiPM on a separate or common silicon substrate.

In one embodiment, detection elements 402 may be grouped into aplurality of regions 404. The regions are geometrical locations orenvironments within sensor 116 (e.g. within detector array 400)—and maybe shaped in different shapes (e.g. rectangular as shown, squares,rings, and so on, or in any other shape). While not all of theindividual detectors, which are included within the geometrical area ofa region 404, necessarily belong to that region, in most cases they willnot belong to other regions 404 covering other areas of the sensor310—unless some overlap is desired in the seams between regions. Asillustrated in FIG. 4A, the regions may be non-overlapping regions 404,but alternatively, they may overlap. Every region may be associated witha regional output circuitry 406 associated with that region. Theregional output circuitry 406 may provide a region output signal of acorresponding group of detection elements 402. For example, the regionof output circuitry 406 may be a summing circuit, but other forms ofcombined output of the individual detector into a unitary output(whether scalar, vector, or any other format) may be employed.Optionally, each region 404 is a single SiPM, but this is notnecessarily so, and a region may be a sub-portion of a single SiPM, agroup of several SiPMs, or even a combination of different types ofdetectors.

In the illustrated example, processing unit 108 is located at aseparated housing 200B (within or outside) host 210 (e.g. within vehicle110), and sensing unit 106 may include a dedicated processor 408 foranalyzing the reflected light. Alternatively, processing unit 108 may beused for analyzing reflected light 206. It is noted that LIDAR system100 may be implemented multiple housings in other ways than theillustrated example. For example, light deflector 114 may be located ina different housing than projecting unit 102 and/or sensing module 106.In one embodiment, LIDAR system 100 may include multiple housingsconnected to each other in different ways, such as: electric wireconnection, wireless connection (e.g., RF connection), fiber opticscable, and any combination of the above.

In one embodiment, analyzing reflected light 206 may include determininga time of flight for reflected light 206, based on outputs of individualdetectors of different regions. Optionally, processor 408 may beconfigured to determine the time of flight for reflected light 206 basedon the plurality of regions of output signals. In addition to the timeof flight, processing unit 108 may analyze reflected light 206 todetermine the average power across an entire return pulse, and thephoton distribution/signal may be determined over the return pulseperiod (“pulse shape”). In the illustrated example, the outputs of anydetection elements 402 may not be transmitted directly to processor 408,but rather combined (e.g. summed) with signals of other detectors of theregion 404 before being passed to processor 408. However, this is onlyan example and the circuitry of sensor 116 may transmit information froma detection element 402 to processor 408 via other routes (not via aregion output circuitry 406).

FIG. 4B is a diagram illustrating LIDAR system 100 configured to scanthe environment of LIDAR system 100 using a two-dimensional sensor 116.In the example of FIG. 4B, sensor 116 is a matrix of 4×6 detectors 410(also referred to as “pixels”). In one embodiment, a pixel size may beabout 1×1 mm. Sensor 116 is two-dimensional in the sense that it hasmore than one set (e.g. row, column) of detectors 410 in twonon-parallel axes (e.g. orthogonal axes, as exemplified in theillustrated examples). The number of detectors 410 in sensor 116 mayvary between differing implementations, e.g. depending on the desiredresolution, signal to noise ratio (SNR), desired detection distance, andso on. For example, sensor 116 may have anywhere between 5 and 5,000pixels. In another example (not shown in the figure) Also, sensor 116may be a one-dimensional matrix (e.g. 1×8 pixels).

It is noted that each detector 410 may include a plurality of detectionelements 402, such as Avalanche Photo Diodes (APD), Single PhotonAvalanche Diodes (SPADs), combination of Avalanche Photo Diodes (APD)and Single Photon Avalanche Diodes (SPADs) or detecting elements thatmeasure both the time of flight from a laser pulse transmission event tothe reception event and the intensity of the received photons. Forexample, each detector 410 may include anywhere between 20 and 5,000SPADs. The outputs of detection elements 402 in each detector 410 may besummed, averaged, or otherwise combined to provide a unified pixeloutput.

In the illustrated example, sensing unit 106 may include atwo-dimensional sensor 116 (or a plurality of two-dimensional sensors116), whose field of view is smaller than field of view 120 of LIDARsystem 100. In this discussion, field of view 120 (the overall field ofview which can be scanned by LIDAR system 100 without moving, rotatingor rolling in any direction) is denoted “first FOV 412”, and the smallerFOV of sensor 116 is denoted “second FOV 412” (interchangeably“instantaneous FOV”). The coverage area of second FOV 414 relative tothe first FOV 412 may differ, depending on the specific use of LIDARsystem 100, and may be, for example, between 0.5% and 50%. In oneexample, second FOV 412 may be between about 0.05° and 1° elongated inthe vertical dimension. Even if LIDAR system 100 includes more than onetwo-dimensional sensor 116, the combined field of view of the sensorsarray may still be smaller than the first FOV 412, e.g. by a factor ofat least 5, by a factor of at least 10, by a factor of at least 20, orby a factor of at least 50, for example.

In order to cover first FOV 412, scanning unit 106 may direct photonsarriving from different parts of the environment to sensor 116 atdifferent times. In the illustrated monostatic configuration, togetherwith directing projected light 204 towards field of view 120 and whenleast one light deflector 114 is located in an instantaneous position,scanning unit 106 may also direct reflected light 206 to sensor 116.Typically, at every moment during the scanning of first FOV 412, thelight beam emitted by LIDAR system 100 covers part of the environmentwhich is larger than the second FOV 414 (in angular opening) andincludes the part of the environment from which light is collected byscanning unit 104 and sensor 116.

FIG. 4C is a diagram illustrating an example of a two-dimensional sensor116. In this embodiment, sensor 116 is a matrix of 8×5 detectors 410 andeach detector 410 includes a plurality of detection elements 402. In oneexample, detector 410A is located in the second row (denoted “R2”) andthird column (denoted “C3”) of sensor 116, which includes a matrix of4×3 detection elements 402. In another example, detector 410B located inthe fourth row (denoted “R4”) and sixth column (denoted “C6”) of sensor116 includes a matrix of 3×3 detection elements 402. Accordingly, thenumber of detection elements 402 in each detector 410 may be constant,or may vary, and differing detectors 410 in a common array may have adifferent number of detection elements 402. The outputs of all detectionelements 402 in each detector 410 may be summed, averaged, or otherwisecombined to provide a single pixel-output value. It is noted that whiledetectors 410 in the example of FIG. 4C are arranged in a rectangularmatrix (straight rows and straight columns), other arrangements may alsobe used, e.g. a circular arrangement or a honeycomb arrangement.

According to some embodiments, measurements from each detector 410 mayenable determination of the time of flight from a light pulse emissionevent to the reception event and the intensity of the received photons.The reception event may be the result of the light pulse being reflectedfrom object 208. The time of flight may be a timestamp value thatrepresents the distance of the reflecting object to optional opticalwindow 124. Time of flight values may be realized by photon detectionand counting methods, such as Time Correlated Single Photon Counters(TCSPC), analog methods for photon detection such as signal integrationand qualification (via analog to digital converters or plaincomparators) or otherwise.

In some embodiments and with reference to FIG. 4B, during a scanningcycle, each instantaneous position of at least one light deflector 114may be associated with a particular portion 122 of field of view 120.The design of sensor 116 enables an association between the reflectedlight from a single portion of field of view 120 and multiple detectors410. Therefore, the scanning resolution of LIDAR system may berepresented by the number of instantaneous positions (per scanningcycle) times the number of detectors 410 in sensor 116. The informationfrom each detector 410 (i.e., each pixel) represents the basic dataelement that from which the captured field of view in thethree-dimensional space is built. This may include, for example, thebasic element of a point cloud representation, with a spatial positionand an associated reflected intensity value. In one embodiment, thereflections from a single portion of field of view 120 that are detectedby multiple detectors 410 may be returning from different objectslocated in the single portion of field of view 120. For example, thesingle portion of field of view 120 may be greater than 50×50 cm at thefar field, which can easily include two, three, or more objects partlycovered by each other.

FIG. 4D is across cut diagram of apart of sensor 116, in accordance withexamples of the presently disclosed subject matter. The illustrated partof sensor 116 includes a part of a detector array 400 which includesfour detection elements 402 (e.g., four SPADs, four APDs). Detectorarray 400 may be a photodetector sensor realized in complementarymetal-oxide-semiconductor (CMOS). Each of the detection elements 402 hasa sensitive area, which is positioned within a substrate surrounding.While not necessarily so, sensor 116 may be used in a monostatic LiDARsystem having a narrow field of view (e.g., because scanning unit 104scans different parts of the field of view at different times). Thenarrow field of view for the incoming light beam—ifimplemented—eliminates the problem of out-of-focus imaging. Asexemplified in FIG. 4D, sensor 116 may include a plurality of lenses 422(e.g., microlenses), each lens 422 may direct incident light toward adifferent detection element 402 (e.g., toward an active area ofdetection element 402), which may be usable when out-of-focus imaging isnot an issue. Lenses 422 may be used for increasing an optical fillfactor and sensitivity of detector array 400, because most of the lightthat reaches sensor 116 may be deflected toward the active areas ofdetection elements 402

Detector array 400, as exemplified in FIG. 4D, may include severallayers built into the silicon substrate by various methods (e.g.,implant) resulting in a sensitive area, contact elements to the metallayers and isolation elements (e.g., shallow trench implant STI, guardrings optical trenches, etc.). The sensitive area may be a volumetricelement in the CMOS detector that enables the optical conversion ofincoming photons into a current flow given an adequate voltage bias isapplied to the device. In the case of a APD/SPAD, the sensitive areawould be a combination of an electrical field that pulls electronscreated by photon absorption towards a multiplication area where aphoton induced electron is amplified creating a breakdown avalanche ofmultiplied electrons.

A front side illuminated detector (e.g., as illustrated in FIG. 4D) hasthe input optical port at the same side as the metal layers residing ontop of the semiconductor (Silicon). The metal layers are required torealize the electrical connections of each individual photodetectorelement (e.g., anode and cathode) with various elements such as: biasvoltage, quenching/ballast elements, and other photodetectors in acommon array. The optical port through which the photons impinge uponthe detector sensitive area is comprised of a passage through the metallayer. It is noted that passage of light from some directions throughthis passage may be blocked by one or more metal layers (e.g., metallayer ML6, as illustrated for the leftmost detector elements 402 in FIG.4D). Such blockage reduces the total optical light absorbing efficiencyof the detector.

FIG. 4E illustrates three detection elements 402, each with anassociated lens 422, in accordance with examples of the presentingdisclosed subject matter. Each of the three detection elements of FIG.4E, denoted 402(1), 402(2), and 402(3), illustrates a lens configurationwhich may be implemented in associated with one or more of the detectingelements 402 of sensor 116. It is noted that combinations of these lensconfigurations may also be implemented.

In the lens configuration illustrated with regards to detection element402(1), a focal point of the associated lens 422 may be located abovethe semiconductor surface. Optionally, openings in different metallayers of the detection element may have different sizes aligned withthe cone of focusing light generated by the associated lens 422. Such astructure may improve the signal-to-noise and resolution of the array400 as a whole device. Large metal layers may be important for deliveryof power and ground shielding. This approach may be useful, e.g., with amonostatic LiDAR design with a narrow field of view where the incominglight beam is comprised of parallel rays and the imaging focus does nothave any consequence to the detected signal.

In the lens configuration illustrated with regards to detection element402(2), an efficiency of photon detection by the detection elements 402may be improved by identifying a sweet spot. Specifically, aphotodetector implemented in CMOS may have a sweet spot in the sensitivevolume area where the probability of a photon creating an avalancheeffect is the highest. Therefore, a focal point of lens 422 may bepositioned inside the sensitive volume area at the sweet spot location,as demonstrated by detection elements 402(2). The lens shape anddistance from the focal point may take into account the refractiveindices of all the elements the laser beam is passing along the way fromthe lens to the sensitive sweet spot location buried in thesemiconductor material.

In the lens configuration illustrated with regards to the detectionelement on the right of FIG. 4E, an efficiency of photon absorption inthe semiconductor material may be improved using a diffuser andreflective elements. Specifically, a near IR wavelength requires asignificantly long path of silicon material in order to achieve a highprobability of absorbing a photon that travels through. In a typicallens configuration, a photon may traverse the sensitive area and may notbe absorbed into a detectable electron. A long absorption path thatimproves the probability for a photon to create an electron renders thesize of the sensitive area towards less practical dimensions (tens of umfor example) for a CMOS device fabricated with typical foundryprocesses. The rightmost detector element in FIG. 4E demonstrates atechnique for processing incoming photons. The associated lens 422focuses the incoming light onto a diffuser element 424. In oneembodiment, light sensor 116 may further include a diffuser located inthe gap distant from the outer surface of at least some of thedetectors. For example, diffuser 424 may steer the light beam sideways(e.g., as perpendicular as possible) towards the sensitive area and thereflective optical trenches 426. The diffuser is located at the focalpoint, above the focal point, or below the focal point. In thisembodiment, the incoming light may be focused on a specific locationwhere a diffuser element is located. Optionally, detector element 422 isdesigned to optically avoid the inactive areas where a photon inducedelectron may get lost and reduce the effective detection efficiency.Reflective optical trenches 426 (or other forms of optically reflectivestructures) cause the photons to bounce back and forth across thesensitive area, thus increasing the likelihood of detection. Ideally,the photons will get trapped in a cavity consisting of the sensitivearea and the reflective trenches indefinitely until the photon isabsorbed and creates an electron/hole pair.

Consistent with the present disclosure, along path is created for theimpinging photons to be absorbed and contribute to a higher probabilityof detection. Optical trenches may also be implemented in detectingelement 422 for reducing cross talk effects of parasitic photons createdduring an avalanche that may leak to other detectors and cause falsedetection events. According to some embodiments, a photo detector arraymay be optimized so that a higher yield of the received signal isutilized, meaning, that as much of the received signal is received andless of the signal is lost to internal degradation of the signal. Thephoto detector array may be improved by: (a) moving the focal point at alocation above the semiconductor surface, optionally by designing themetal layers above the substrate appropriately; (b) by steering thefocal point to the most responsive/sensitive area (or “sweet spot”) ofthe substrate and (c) adding a diffuser above the substrate to steer thesignal toward the “sweet spot” and/or adding reflective material to thetrenches so that deflected signals are reflected back to the “sweetspot.”

While in some lens configurations, lens 422 may be positioned so thatits focal point is above a center of the corresponding detection element402, it is noted that this is not necessarily so. In other lensconfiguration, a position of the focal point of the lens 422 withrespect to a center of the corresponding detection element 402 isshifted based on a distance of the respective detection element 402 froma center of the detection array 400. This may be useful in relativelylarger detection arrays 400, in which detector elements further from thecenter receive light in angles which are increasingly off-axis. Shiftingthe location of the focal points (e.g., toward the center of detectionarray 400) allows correcting for the incidence angles. Specifically,shifting the location of the focal points (e.g., toward the center ofdetection array 400) allows correcting for the incidence angles whileusing substantially identical lenses 422 for all detection elements,which are positioned at the same angle with respect to a surface of thedetector.

Adding an array of lenses 422 to an array of detection elements 402 maybe useful when using a relatively small sensor 116 which covers only asmall part of the field of view because in such a case, the reflectionsignals from the scene reach the detectors array 400 from substantiallythe same angle, and it is, therefore, easy to focus all the light ontoindividual detectors. It is also noted, that in one embodiment, lenses422 may be used in LIDAR system 100 for favoring about increasing theoverall probability of detection of the entire array 400 (preventingphotons from being “wasted” in the dead area betweendetectors/sub-detectors) at the expense of spatial distinctiveness. Thisembodiment is in contrast to prior art implementations such as CMOS RGBcamera, which prioritize spatial distinctiveness (i.e., light thatpropagates in the direction of detection element A is not allowed to bedirected by the lens toward detection element B, that is, to “bleed” toanother detection element of the array). Optionally, sensor 116 includesan array of lens 422, each being correlated to a corresponding detectionelement 402, while at least one of the lenses 422 deflects light whichpropagates to a first detection element 402 toward a second detectionelement 402 (thereby it may increase the overall probability ofdetection of the entire array).

Specifically, consistent with some embodiments of the presentdisclosure, light sensor 116 may include an array of light detectors(e.g., detector array 400), each light detector (e.g., detector 410)being configured to cause an electric current to flow when light passesthrough an outer surface of a respective detector. In addition, lightsensor 116 may include at least one micro-lens configured to directlight toward the array of light detectors, the at least one micro-lenshaving a focal point. Light sensor 116 may further include at least onelayer of conductive material interposed between the at least onemicro-lens and the array of light detectors and having a gap therein topermit light to pass from the at least one micro-lens to the array, theat least one layer being sized to maintain a space between the at leastone micro-lens and the array to cause the focal point (e.g., the focalpoint may be a plane) to be located in the gap, at a location spacedfrom the detecting surfaces of the array of light detectors.

In related embodiments, each detector may include a plurality of SinglePhoton Avalanche Diodes (SPADs) or a plurality of Avalanche Photo Diodes(APD). The conductive material may be a multi-layer metal constriction,and the at least one layer of conductive material may be electricallyconnected to detectors in the array. In one example, the at least onelayer of conductive material includes a plurality of layers. Inaddition, the gap may be shaped to converge from the at least onemicro-lens toward the focal point, and to diverge from a region of thefocal point toward the array. In other embodiments, light sensor 116 mayfurther include at least one reflector adjacent each photo detector. Inone embodiment, a plurality of micro-lenses may be arranged in a lensarray and the plurality of detectors may be arranged in a detectorarray. In another embodiment, the plurality of micro-lenses may includea single lens configured to project light to a plurality of detectors inthe array.

The Processing Unit

FIGS. 5A-5C depict different functionalities of processing units 108 inaccordance with some embodiments of the present disclosure.Specifically, FIG. 5A is a diagram illustrating emission patterns in asingle frame-time for a single portion of the field of view, FIG. 5B isa diagram illustrating emission scheme in a single frame-time for thewhole field of view, and. FIG. 5C is a diagram illustrating the actuallight emission projected towards field of view during a single scanningcycle.

FIG. 5A illustrates four examples of emission patterns in a singleframe-time for a single portion 122 of field of view 120 associated withan instantaneous position of at least one light deflector 114.Consistent with embodiments of the present disclosure, processing unit108 may control at least one light source 112 and light deflector 114(or coordinate the operation of at least one light source 112 and atleast one light deflector 114) in a manner enabling light flux to varyover a scan of field of view 120. Consistent with other embodiments,processing unit 108 may control only at least one light source 112 andlight deflector 114 may be moved or pivoted in a fixed predefinedpattern.

Diagrams A-D in FIG. 5A depict the power of light emitted towards asingle portion 122 of field of view 120 over time. In Diagram A,processor 118 may control the operation of light source 112 in a mannersuch that during scanning of field of view 120 an initial light emissionis projected toward portion 122 of field of view 120. When projectingunit 102 includes a pulsed-light light source, the initial lightemission may include one or more initial pulses (also referred to as“pilot pulses”). Processing unit 108 may receive from sensor 116 pilotinformation about reflections associated with the initial lightemission. In one embodiment, the pilot information may be represented asa single signal based on the outputs of one or more detectors (e.g. oneor more SPADs, one or more APDs, one or more SiPMs, etc.) or as aplurality of signals based on the outputs of multiple detectors. In oneexample, the pilot information may include analog and/or digitalinformation. In another example, the pilot information may include asingle value and/or a plurality of values (e.g. for different timesand/or parts of the segment).

Based on information about reflections associated with the initial lightemission, processing unit 108 may be configured to determine the type ofsubsequent light emission to be projected towards portion 122 of fieldof view 120. The determined subsequent light emission for the particularportion of field of view 120 may be made during the same scanning cycle(i.e., in the same frame) or in a subsequent scanning cycle (i.e., in asubsequent frame). This embodiment is described in greater detail belowwith reference to FIGS. 23-25.

In Diagram B, processor 118 may control the operation of light source112 in a manner such that during scanning of field of view 120 lightpulses in different intensities are projected towards a single portion122 of field of view 120. In one embodiment, LIDAR system 100 may beoperable to generate depth maps of one or more different types, such asany one or more of the following types: point cloud model, polygon mesh,depth image (holding depth information for each pixel of an image or ofa 2D array), or any other type of 3D model of a scene. The sequence ofdepth maps may be a temporal sequence, in which different depth maps aregenerated at a different time. Each depth map of the sequence associatedwith a scanning cycle (interchangeably “frame”) may be generated withinthe duration of a corresponding subsequent frame-time. In one example, atypical frame-time may last less than a second. In some embodiments,LIDAR system 100 may have a fixed frame rate (e.g. 10 frames per second,25 frames per second, 50 frames per second) or the frame rate may bedynamic. In other embodiments, the frame-times of different frames maynot be identical across the sequence. For example, LIDAR system 100 mayimplement a 10 frames-per-second rate that includes generating a firstdepth map in 100 milliseconds (the average), a second frame in 92milliseconds, a third frame at 142 milliseconds, and so on.

In Diagram C, processor 118 may control the operation of light source112 in a manner such that during scanning of field of view 120 lightpulses associated with different durations are projected towards asingle portion 122 of field of view 120. In one embodiment, LIDAR system100 may be operable to generate a different number of pulses in eachframe. The number of pulses may vary between 0 to 32 pulses (e.g., 1, 5,12, 28, or more pulses) and may be based on information derived fromprevious emissions. The time between light pulses may depend on desireddetection range and can be between 500 ns and 5,000 ns. In one example,processing unit 108 may receive from sensor 116 information aboutreflections associated with each light-pulse. Based on the information(or the lack of information), processing unit 108 may determine ifadditional light pulses are needed. It is noted that the durations ofthe processing times and the emission times in diagrams A-D are notin-scale. Specifically, the processing time may be substantially longerthan the emission time. In diagram D, projecting unit 102 may include acontinuous-wave light source. In one embodiment, the initial lightemission may include a period of time where light is emitted and thesubsequent emission may be a continuation of the initial emission, orthere may be a discontinuity. In one embodiment, the intensity of thecontinuous emission may change over time.

Consistent with some embodiments of the present disclosure, the emissionpattern may be determined per each portion of field of view 120. Inother words, processor 118 may control the emission of light to allowdifferentiation in the illumination of different portions of field ofview 120. In one example, processor 118 may determine the emissionpattern for a single portion 122 of field of view 120, based ondetection of reflected light from the same scanning cycle (e.g., theinitial emission), which makes LIDAR system 100 extremely dynamic. Inanother example, processor 118 may determine the emission pattern for asingle portion 122 of field of view 120, based on detection of reflectedlight from a previous scanning cycle. The differences in the patterns ofthe subsequent emissions may result from determining different valuesfor light-source parameters for the subsequent emission, such as any oneof the following.

-   -   a. Overall energy of the subsequent emission.    -   b. Energy profile of the subsequent emission.    -   c. A number of light-pulse-repetition per frame.    -   d. Light modulation characteristics such as duration, rate,        peak, average power, and pulse shape.    -   e. Wave properties of the subsequent emission, such as        polarization, wavelength, etc.

Consistent with the present disclosure, the differentiation in thesubsequent emissions may be put to different uses. In one example, it ispossible to limit emitted power levels in one portion of field of view120 where safety is a consideration, while emitting higher power levels(thus improving signal-to-noise ratio and detection range) for otherportions of field of view 120. This is relevant for eye safety, but mayalso be relevant for skin safety, safety of optical systems, safety ofsensitive materials, and more. In another example, it is possible todirect more energy towards portions of field of view 120 where it willbe of greater use (e.g. regions of interest, further distanced targets,low reflection targets, etc.) while limiting the lighting energy toother portions of field of view 120 based on detection results from thesame frame or previous frame. It is noted that processing unit 108 mayprocess detected signals from a single instantaneous field of viewseveral times within a single scanning frame time; for example,subsequent emission may be determined upon after every pulse emitted, orafter a number of pulses emitted.

FIG. 5B illustrates three examples of emission schemes in a singleframe-time for field of view 120. Consistent with embodiments of thepresent disclosure, at least on processing unit 108 may use obtainedinformation to dynamically adjust the operational mode of LIDAR system100 and/or determine values of parameters of specific components ofLIDAR system 100. The obtained information may be determined fromprocessing data captured in field of view 120, or received (directly orindirectly) from host 210. Processing unit 108 may use the obtainedinformation to determine a scanning scheme for scanning the differentportions of field of view 120. The obtained information may include acurrent light condition, a current weather condition, a current drivingenvironment of the host vehicle, a current location of the host vehicle,a current trajectory of the host vehicle, a current topography of roadsurrounding the host vehicle, or any other condition or objectdetectable through light reflection. In some embodiments, the determinedscanning scheme may include at least one of the following: (a) adesignation of portions within field of view 120 to be actively scannedas part of a scanning cycle, (b) a projecting plan for projecting unit102 that defines the light emission profile at different portions offield of view 120; (c) a deflecting plan for scanning unit 104 thatdefines, for example, a deflection direction, frequency, and designatingidle elements within a reflector array; and (d) a detection plan forsensing unit 106 that defines the detectors sensitivity or responsivitypattern.

In addition, processing unit 108 may determine the scanning scheme atleast partially by obtaining an identification of at least one region ofinterest within the field of view 120 and at least one region ofnon-interest within the field of view 120. In some embodiments,processing unit 108 may determine the scanning scheme at least partiallyby obtaining an identification of at least one region of high interestwithin the field of view 120 and at least one region of lower-interestwithin the field of view 120. The identification of the at least oneregion of interest within the field of view 120 may be determined, forexample, from processing data captured in field of view 120, based ondata of another sensor (e.g. camera. GPS), received (directly orindirectly) from host 210, or any combination of the above. In someembodiments, the identification of at least one region of interest mayinclude identification of portions, areas, sections, pixels, or objectswithin field of view 120 that are important to monitor. Examples ofareas that may be identified as regions of interest may include,crosswalks, moving objects, people, nearby vehicles or any otherenvironmental condition or object that may be helpful in vehiclenavigation. Examples of areas that may be identified as regions ofnon-interest (or lower-interest) may be static (non-moving) far-awaybuildings, a skyline, an area above the horizon and objects in the fieldof view. Upon obtaining the identification of at least one region ofinterest within the field of view 120, processing unit 108 may determinethe scanning scheme or change an existing scanning scheme. Further todetermining or changing the light-source parameters (as describedabove), processing unit 108 may allocate detector resources based on theidentification of the at least one region of interest. In one example,to reduce noise, processing unit 108 may activate detectors 410 where aregion of interest is expected and disable detectors 410 where regionsof non-interest are expected. In another example, processing unit 108may change the detector sensitivity, e.g., increasing sensor sensitivityfor long range detection where the reflected power is low.

Diagrams A-C in FIG. 5B depict examples of different scanning schemesfor scanning field of view 120. Each square in field of view 120represents a different portion 122 associated with an instantaneousposition of at least one light deflector 114. Legend 500 details thelevel of light flux represented by the filling pattern of the squares.Diagram A depicts a first scanning scheme in which all of the portionshave the same importance/priority and a default light flux is allocatedto them. The first scanning scheme may be utilized in a start-up phaseor periodically interleaved with another scanning scheme to monitor thewhole field of view for unexpected/new objects. In one example, thelight source parameters in the first scanning scheme may be configuredto generate light pulses at constant amplitudes. Diagram B depicts asecond scanning scheme in which a portion of field of view 120 isallocated with high light flux while the rest of field of view 120 isallocated with default light flux and low light flux. The portions offield of view 120 that are the least interesting may be allocated withlow light flux. Diagram C depicts a third scanning scheme in which acompact vehicle and a bus (see silhouettes) are identified in field ofview 120. In this scanning scheme, the edges of the vehicle and bus maybe tracked with high power and the central mass of the vehicle and busmay be allocated with less light flux (or no light flux). Such lightflux allocation enables concentration of more of the optical budget onthe edges of the identified objects and less on their center which haveless importance.

FIG. 5C illustrating the emission of light towards field of view 120during a single scanning cycle. In the depicted example, field of view120 is represented by an 8×9 matrix, where each of the 72 cellscorresponds to a separate portion 122 associated with a differentinstantaneous position of at least one light deflector 114. In thisexemplary scanning cycle, each portion includes one or more white dotsthat represent the number of light pulses projected toward that portion,and some portions include black dots that represent reflected light fromthat portion detected by sensor 116. As shown, field of view 120 isdivided into three sectors: sector I on the right side of field of view120, sector II in the middle of field of view 120, and sector III on theleft side of field of view 120. In this exemplary scanning cycle, sectorI was initially allocated with a single light pulse per portion; sectorII, previously identified as a region of interest, was initiallyallocated with three light pulses per portion; and sector III wasinitially allocated with two light pulses per portion. Also as shown,scanning of field of view 120 reveals four objects 208: two free-formobjects in the near field (e.g., between 5 and 50 meters), arounded-square object in the mid field (e.g., between 50 and 150meters), and a triangle object in the far field (e.g., between 150 and500 meters). While the discussion of FIG. 5C uses number of pulses as anexample of light flux allocation, it is noted that light flux allocationto different parts of the field of view may also be implemented in otherways such as: pulse duration, pulse angular dispersion, wavelength,instantaneous power, photon density at different distances from lightsource 112, average power, pulse power intensity, pulse width, pulserepetition rate, pulse sequence, pulse duty cycle, wavelength, phase,polarization, and more. The illustration of the light emission as asingle scanning cycle in FIG. 5C demonstrates different capabilities ofLIDAR system 100. In a first embodiment, processor 118 is configured touse two light pulses to detect a first object (e.g., the rounded-squareobject) at a first distance, and to use three light pulses to detect asecond object (e.g., the triangle object) at a second distance greaterthan the first distance. This embodiment is described in greater detailbelow with reference to FIGS. 11-13. In a second embodiment, processor118 is configured to allocate more light to portions of the field ofview where a region of interest is identified. Specifically, in thepresent example, sector II was identified as a region of interest andaccordingly it was allocated with three light pulses while the rest offield of view 120 was allocated with two or less light pulses. Thisembodiment is described in greater detail below with reference to FIGS.20-22. In a third embodiment, processor 118 is configured to controllight source 112 in a manner such that only a single light pulse isprojected toward to portions B1, B2, and C1 in FIG. 5C, although theyare part of sector III that was initially allocated with two lightpulses per portion. This occurs because the processing unit 108 detectedan object in the near field based on the first light pulse. Thisembodiment is described in greater detail below with reference to FIGS.23-25. Allocation of less than maximal amount of pulses may also be aresult of other considerations. For examples, in at least some regions,detection of object at a first distance (e.g. a near field object) mayresult in reducing an overall amount of light emitted to this portion offield of view 120. This embodiment is described in greater detail belowwith reference to FIGS. 14-16. Other reasons for determining powerallocation to different portions is discussed below with respect toFIGS. 29-31, FIGS. 53-55, and FIGS. 50-52. Additional details andexamples on different components of LIDAR system 100 and theirassociated functionalities are included in Applicant's U.S. patentapplication Ser. No. 15/391,916 filed Dec. 28, 2016; Applicant's U.S.patent application Ser. No. 15/393,749 filed Dec. 29, 2016; Applicant'sU.S. patent application Ser. No. 15/393,285 filed Dec. 29, 2016; andApplicant's U.S. patent application Ser. No. 15/393,593 filed Dec. 29,2016, which are incorporated herein by reference in their entirety.

Example Implementation: Vehicle

FIGS. 6A-6C illustrate the implementation of LIDAR system 100 in avehicle (e.g., vehicle 110). Any of the aspects of LIDAR system 100described above or below may be incorporated into vehicle 110 to providea range-sensing vehicle. Specifically, in this example, LIDAR system 100integrates multiple scanning units 104 and potentially multipleprojecting units 102 in a single vehicle. In one embodiment, a vehiclemay take advantage of such a LIDAR system to improve power, range andaccuracy in the overlap zone and beyond it, as well as redundancy insensitive parts of the FOV (e.g. the forward movement direction of thevehicle). As shown in FIG. 6A, vehicle 110 may include a first processor118A for controlling the scanning of field of view 120A, a secondprocessor 118B for controlling the scanning of field of view 120B, and athird processor 118C for controlling synchronization of scanning the twofields of view. In one example, processor 118C may be the vehiclecontroller and may have a shared interface between first processor 118Aand second processor 118B. The shared interface may enable an exchangingof data at intermediate processing levels and a synchronization ofscanning of the combined field of view in order to form an overlap inthe temporal and/or spatial space. In one embodiment, the data exchangedusing the shared interface may be: (a) time of flight of receivedsignals associated with pixels in the overlapped field of view and/or inits vicinity; (b) laser steering position status; (c) detection statusof objects in the field of view.

FIG. 6B illustrates overlap region 600 between field of view 120A andfield of view 120B. In the depicted example, the overlap region isassociated with 24 portions 122 from field of view 120A and 24 portions122 from field of view 120B. Given that the overlap region is definedand known by processors 118A and 118B, each processor may be designed tolimit the amount of light emitted in overlap region 600 in order toconform with an eye safety limit that spans multiple source lights, orfor other reasons such as maintaining an optical budget. In addition,processors 118A and 118B may avoid interferences between the lightemitted by the two light sources by loose synchronization between thescanning unit 104A and scanning unit 104B, and/or by control of thelaser transmission timing, and/or the detection circuit enabling timing.

FIG. 6C illustrates how overlap region 600 between field of view 120Aand field of view 120B may be used to increase the detection distance ofvehicle 110. Consistent with the present disclosure, two or more lightsources 112 projecting their nominal light emission into the overlapzone may be leveraged to increase the effective detection range. Theterm “detection range” may include an approximate distance from vehicle110 at which LIDAR system 100 can clearly detect an object. In oneembodiment, the maximum detection range of LIDAR system 100 is about 300meters, about 400 meters, or about 500 meters. For example, for adetection range of 200 meters, LIDAR system 100 may detect an objectlocated 200 meters (or less) from vehicle 110 at more than 95%, morethan 99%, more than 99.5% of the times. Even when the object'sreflectivity may be less than 50% (e.g., less than 20%, less than 10%,or less than 5%). In addition, LIDAR system 100 may have less than 1%false alarm rate. In one embodiment, light from projected from two lightsources that are collocated in the temporal and spatial space can beutilized to improve SNR and therefore increase the range and/or qualityof service for an object located in the overlap region. Processor 118Cmay extract high-level information from the reflected light in field ofview 120A and 120B. The term “extracting information” may include anyprocess by which information associated with objects, individuals,locations, events, etc., is identified in the captured image data by anymeans known to those of ordinary skill in the art. In addition,processors 118A and 118B may share the high-level information, such asobjects (road delimiters, background, pedestrians, vehicles, etc.), andmotion vectors, to enable each processor to become alert to theperipheral regions about to become regions of interest. For example, amoving object in field of view 120A may be determined to soon beentering field of view 120B.

Example Implementation: Surveillance system

FIG. 6D illustrates the implementation of LIDAR system 100 in asurveillance system. As mentioned above, LIDAR system 100 may be fixedto a stationary object 650 that may include a motor or other mechanismsfor rotating the housing of the LIDAR system 100 to obtain a wider fieldof view. Alternatively, the surveillance system may include a pluralityof LIDAR units. In the example depicted in FIG. 6D, the surveillancesystem may use a single rotatable LIDAR system 100 to obtain 3D datarepresenting field of view 120 and to process the 3D data to detectpeople 652, vehicles 654, changes in the environment, or any other formof security-significant data.

Consistent with some embodiment of the present disclosure, the 3D datamay be analyzed to monitor retail business processes. In one embodiment,the 3D data may be used in retail business processes involving physicalsecurity (e.g., detection of: an intrusion within a retail facility, anact of vandalism within or around a retail facility, unauthorized accessto a secure area, and suspicious behavior around cars in a parking lot).In another embodiment, the 3D data may be used in public safety (e.g.,detection of: people slipping and falling on store property, a dangerousliquid spill or obstruction on a store floor, an assault or abduction ina store parking lot, an obstruction of a fire exit, and crowding in astore area or outside of the store). In another embodiment, the 3D datamay be used for business intelligence data gathering (e.g., tracking ofpeople through store areas to determine, for example, how many people gothrough, where they dwell, how long they dwell, how their shoppinghabits compare to their purchasing habits).

Consistent with other embodiments of the present disclosure, the 3D datamay be analyzed and used for traffic enforcement. Specifically, the 3Ddata may be used to identify vehicles traveling over the legal speedlimit or some other road legal requirement. In one example, LIDAR system100 may be used to detect vehicles that cross a stop line or designatedstopping place while a red traffic light is showing. In another example,LIDAR system 100 may be used to identify vehicles traveling in lanesreserved for public transportation. In yet another example, LIDAR system100 may be used to identify vehicles turning in intersections wherespecific turns are prohibited on red.

It should be noted that while examples of various disclosed embodimentshave been described above and below with respect to a control unit thatcontrols scanning of a deflector, the various features of the disclosedembodiments are not limited to such systems. Rather, the techniques forallocating light to various portions of a LIDAR FOV may be applicable totype of light-based sensing system (LIDAR or otherwise) in which theremay be a desire or need to direct different amounts of light todifferent portions of field of view. In some cases, such lightallocation techniques may positively impact detection capabilities, asdescribed herein, but other advantages may also result.

Detecting Objects Based on Reflectivity Fingerprints

In addition to determining distances based on reflected pulses, LIDARsystems disclosed herein may further determine the reflectivity ofportions of a field of view based on reflected pulses. For example, theintensity of the reflected pulses may be normalized based on determineddistances in order to determine reflectivity values. The determinedreflectivities may be used for object identification (e.g., using a“reflectivity fingerprint”).

Systems of the present disclosure may provide more detailed feedbackthan extant systems. For example, LIDAR systems of the presentdisclosure may output any combination of one or more of: reflectivity(e.g., an illumination level, optionally corrected for distance and thelike), surface angles (e.g., based on single pixel information and/orinformation across pixels), confidence levels (e.g., associated withdetections or with other parameters such as reflectivity, surfaceangles, etc.), ambient light level (e.g., measurements taken withoutillumination from the LIDAR), properties of detected objects (e.g.,whether the object is metal), or the like. In addition, LIDAR systems ofthe present disclosure may allow for determination of changes in any ofthe outputs as compared with one or more previous frames (e.g., changesin reflectivity, changes in confidence levels, changes in ambient light,or the like). In some embodiments, this additional information may beused directly to detect objects and determine properties of detectedobjects. Additionally or alternatively, this additional information maybe used to construct and/or analyze point cloud maps (an example ofwhich is described above with respect to FIG. 1C).

In some embodiments, objects detected by a LIDAR system may possessreflectivity patterns that allow for identification based on knownpatterns. For example, other vehicles may possess recognizablereflectivity patterns based on highly reflective components such asheadlamps, taillamps, license plates, or the like. By identifyingobjects using reflectivity patterns, objects may be identified at largerdistances and/or with less data than using extant LIDAR identificationtechniques. Some non-limiting examples of “less data” in the presentcontext include: fewer point-cloud points, point-clouds with lowerconfidence levels (e.g., of detection, ranges, or other parameters),point-cloud points for which less parameters have been determined, orthe like.

As used herein, “reflectivity” may refer to the detection level outputby one or more sensors of the LIDAR but may also refer to a detectionlevel that has been corrected or normalized (e.g., corrected fordistance between a light source of the LIDAR and a detected object, suchas dividing by R or by R², or the like). By combining (optionallycorrected or normalized) reflectivity values with a spatial point cloudmap, systems of the present disclosure may allow for more accuratedetection, identification and characterization of objects than extanttechniques. In addition, systems of the present disclosure may allow fordetection, identification and characterization of objects that mayotherwise be undetectable and/or unidentifiable with extant techniques.Characterization may include, for example, assessing the spatialdimensions of a detected or identified objects, assessing of itsprogression direction and velocity, or the like.

FIG. 7 depicts an example method 700 for detecting a vehicle based onlicense plate reflectivity, in accordance with examples of the presentlydisclosed subject matter. Method 700 may be executed by at least oneprocessor, such as a processor of a LIDAR system (e.g., at least oneprocessor 118 of processing unit 108 in the example of FIG. 1A, twoprocessors 118 of processing unit 108 of FIG. 2A, any one or more ofprocessing units 108 of FIGS. 5A-5C, or the like). It is noted thatmethod 700 may be implemented by any type of LIDAR system, and notnecessarily by LIDAR system 100. For example, method 700 may be executedby a scanning or a non-scanning LIDAR system, by a pulsed orcontinuous-wave LIDAR system, or the like.

At step 701, the at least one processor scans a field of view bycontrolling movement of at least one deflector at which at least onelight source is directed. For example, the at least one processor maycontrol movement of light deflector 114 of FIG. 1A, light deflectors114A and 114B of FIG. 2A, light deflectors 216 of FIG. 2B, lightdeflector 216 of FIG. 2C, or the like. In some embodiments, step 701 maybe omitted from method 700. For example, method 700 may be performedwith a LIDAR system that is stationary (also referred to as “fixed” or“staring”) rather than scanning.

In some embodiments, the at least one processor may further control theat least one light source in a manner enabling light flux of lightprojected from the at least one light source to vary during scanning ofthe field of view. For example, the at least one processor may vary thetiming of pulses from the at least one light source. Alternatively orconcurrently, the at least one processor may vary the length of pulsesfrom the at least one light source. By way of further example, the atleast one processor may alternatively or concurrently vary a size (e.g.,length or width or otherwise alter a cross-sectional area) of pulsesfrom the at least one light source. In a yet further example, the atleast one processor may alternatively or concurrently vary the amplitudeand/or frequency of pulses from the at least one light source. Incertain aspects, the at least one processor may vary the light fluxduring a single scan and/or across a plurality of scans. Additionally oralternatively, the at least one processor may vary the light flux acrossa plurality of regions in the field of view (e.g., during a scan and/oracross a plurality of scans).

In some embodiments, method 700 may further include controlling the atleast one light deflector such that during a scanning cycle of the fieldof view, the at least one light deflector instantaneously assumes aplurality of instantaneous positions. In one example, the at least oneprocessor may coordinate the at least one light deflector and the atleast one light source such that when the at least one light deflectorassumes a particular instantaneous position, a portion of a light beamis deflected by the at least one light deflector from the at least onelight source towards an object in the field of view, and reflections ofthe portion of the light beam from the object are deflected by the atleast one light deflector toward at least one sensor. In anotherexample, the at least one light source may comprise a plurality oflights sources aimed at the at least one light deflector, and the atleast one processor may control the at least one light deflector suchthat when the at least one light deflector assumes a particularinstantaneous position, light from the plurality of light sources isprojected towards a plurality of independent regions in the field ofview.

In other embodiments, method 700 may be performed without varying thelight flux of the at least one light source. For example, method 700 maybe performed with a LIDAR system that is fixed-power rather thanvariable-power.

Optionally, the at least one processor may further control the at leastone light source in a manner enabling modulating the projected light anddistinguishing between light reflected from objects in the field of viewand light emitted by objects in the field of view. For example, the atleast one processor may pulse the at least one light source such thatthe gap between pulses is sufficiently long to receive light emitted byobjects in the field of view without any reflections from objects in thefield of view of light emitted by the LIDAR system.

At step 703, the at least one processor receives from at least onesensor signals indicative of light reflected from a particular object inthe field of view. For example, the at least one processor may receivethe signals from the sensor of the LIDAR system (e.g., sensor 116 ofFIG. 1A or FIG. 2A, sensors 116 of FIG. 2B, sensor 116 of FIG. 2C, orthe like). In some embodiments, the signals may comprise raw signal datafrom the at least one sensor.

In other embodiments, one or more processors (e.g., the at least oneprocessor and/or one or more other processors) may first perform one ormore operations on the raw signal data. For example, as discussed above,the one or more operations may include correction and/or normalizationbased on a detected distance for each signal. In another example, theone or more operations may include correction and/or normalization fornoise (e.g., based on an expected level of noise, based on a measuredlevel of noise, based on expected signal level and/or based on ameasured signal level). In yet another example, the one or moreoperations may include application of one or more filters (e.g., a lowpass filter, a bandpass filter, and/or a high pass filter).

It is noted that steps 701 and 703 are optional, and may alternativelybe executed by another system than the system which utilizes thedetection results for object classification. For example, steps 701and/or 703 may be executed by a LIDAR sensing system (e.g., located atthe front of a vehicle having the LIDAR system) that may process thesensor detection to provide a 3D model of the surrounding while steps705, 707, and 709 may be executed by a LIDAR system that may process theinputs to classify object, such as a host system of the vehicle. It isnoted that the system which executes steps 705, 707, and 709 may alsoutilize other sensor inputs (e.g., camera, radar, etc.) to analyze theenvironment of the vehicle in addition to the LIDAR detection results.In embodiments where steps 701 and 703 are executed by another system,method 700 may alternatively begin with receiving detection results froma LIDAR sensing system including reflectivity information associatedwith different locations in the environment of the LIDAR sensing system.

At step 705, the at least one processor detects portions of theparticular object in the field of view that are similarly spaced fromthe light source. For example, the detection of the portions of theparticular object that are similarly spaced from the light source may bebased on time of flight of the received signals (which may be calculatedbased on a difference between a time of illumination—e.g., a time ofsending one or more pulses from the at least one light source—and a timeof detection—e.g., a time that one or more reflected pulses are absorbedon the at least one sensor). The detection of the portions of theparticular object that are similarly spaced from the light source mayadditionally or alternatively be based on location informationdetermined for different locations in the environment of the LIDARsystem by analysis of the LIDAR detection results (e.g., point-cloudpoints).

Optionally, the at least one processor may first detect (e.g., identifyboundaries of) the particular object. In one example, the at least oneprocessor may construct a point cloud map from the received signals anddetect the particular object therewith. In another example, the at leastone processor may use one or more properties of the received signals(e.g., brightness, color, etc.) to detect the particular object. Inother embodiments, the at least one processor may detect the similarlyspaced portions without first detecting the particular object.

As used herein. “similarly spaced” may refer to portions that are at thesame distance from the light source or to portions that are at distancesfrom the light source within a particular threshold (e.g., 0.2 m, 1 m, 2m, within 2% of the shortest distance, 5% of each other, or the like).It is noted that step 705 may additionally or alternatively includedetecting the portions of the particular object which—in addition tobeing similarly spaced from the light source—are also grouped in arelatively small volume. For example, these portions may also bedetected in proximate viewing angle of the LIDAR system, (e.g., within a1°-1° portion of the FOV, within a 2°-2° portion of the FOV) and/orwithin a limited distance from one another (e.g., less than 1 m, 2 m, 5m, etc. from one another, in any direction).

At step 707, the at least one processor determines, based on thedetected portions, at least a first portion having a first reflectivitycorresponding to a license plate, and at least two additionalspaced-apart portions corresponding to locations on the particularobject other than a location of the first portion, and wherein the atleast two additional portions have reflectivity substantially lower thanthe first reflectivity. For example, the first reflectivity may bewithin a preset range such that the at least one processor classifiesthe portion as a license plate. In another example, the firstreflectivity may be higher (e.g., two times, five times, etc.) than thereflectivity of a surrounding area, the maximum reflectivity of thesurrounding area and/or the rest of the received signals, or astatistical value derived from the received signals or a portion thereof(e.g., corresponding to the surrounding area). The statistical value mayinclude a mean, a median, a standard deviation, or the like.

Optionally, the at least two additional portions may have the same (orsubstantially the same, e.g., within 2%, within 5%, etc.) reflectivity.In other embodiments, the at least two additional portions may havediffering reflectivities, each of which is substantially lower than thefirst reflectivity. As used herein, “substantially lower” may refer to avalue lower as measured by a particular threshold (e.g., at least 20%lower, at least 25% lower, at least 50% lower, or the like). Althoughhaving a lower reflectivity value than the first portion, the secondportion and the third portion generally have sufficiently highreflectivity to be detected. Optionally, the portions selected as thesecond portion and third portion (as well as any other potentiallyselected portion of the particular object) may be required to meet aminimal reflectivity criterion. It is further noted that method 700 maybe used for particular objects whose overall reflection is generally low(e.g., because of color, distance from the LIDAR system, weather, or thelike) such that only a few portions of the particular object are evendetected.

As used herein, “spaced-apart portions corresponding to locations on theparticular object other than a location of the first portion” indicatesportions that are excluded from each other as well as from the licenseplate. Furthermore, as used herein, “reflectivity” may refer toreflectivity by active illumination by LIDAR as well as reflectivity atthe same wavelength as the LIDAR.

Additionally or alternatively, at step 707, the at least one processoridentifies based on the detected portions, at least a first portionhaving a first reflectivity, a second portion having a secondreflectivity, and a third portion having a third reflectivity, andwherein the at least second and third portions have reflectivitysubstantially lower than the first reflectivity.

At step 709, based on a spatial relationship and a reflectivityrelationship between the first portion and the at least two additionalportions, the at least one processor classifies the particular object asa vehicle. Accordingly, the spatial relationship and the reflectivityrelationship may form a “reflectivity fingerprint” (or a part of a“reflectivity fingerprint”) used to classify the particular object as avehicle. For example, the spatial relationship may comprise one or morelengths between the first portions and the at least two additionalportions (whether center-center, center-edge, edge-edge, or the like),one or more angles between the first portions and the at least twoadditional portions (or between a connecting line drawn center-center,center-edge, edge-edge, or the like), one or more sizes of the firstportions and the at least two additional portions (and/or relationsbetween the one or more sizes), or the like. Meanwhile, the reflectivityrelationship may comprise an absolute difference between the firstreflectivity and the reflectivities of the at least two additionalportions, a scaled difference (e.g., twice, thrice, etc.) between thefirst reflectivity and the reflectivities of the at least two additionalportions, or the like. Additionally or alternatively, the reflectivityrelationship may comprise an absolute difference between the secondreflectivity and the third reflectivity, a scaled difference (e.g.,twice, thrice, etc.) between the second reflectivity and the thirdreflectivity, or the like.

In one example, the object may be classified as a vehicle when the firstportion is between the at least two additional portions. In addition,the object may be classified as a vehicle when the at least twoadditional portions are located at similar distances from either side ofthe first portion consistent with expected locations of taillamps (orheadlamps).

Additionally or alternatively, at step 709, the at least one processordetermines a reflectivity fingerprint of the particular object based ona reflectivity relationship between the first portion, the secondportion, and the third portion. As explained above, the reflectivityfingerprint may include the reflectivities of the first portion, thesecond portion, and the third portion and may further include a spatialrelationship between the first portion, the second portion, and thethird portion and/or sizes of the first portion, the second portion, andthe third portion. In such embodiments, the at least one processor mayclassify the particular object based on the determined reflectivityfingerprint of the particular object.

Similarly, the at least one processor may use a fingerprint includingreflectivity in addition to ambient light level for identification ofvehicles. For example, the first portion (corresponding to a licenseplate) may have high reflectivity while the second and third portions(corresponding to taillamps or headlamps) may have a high ambient lightlevel. In another example, the first portion (corresponding to a licenseplate) may have high reflectivity as well as a low ambient light levelwhile the second and third portions (corresponding to taillamps orheadlamps) may have lower reflectivity and a high ambient light level.These examples may be more accurate and/or efficient for identifyingvehicles when taillamps or headlamps are illuminated while using onlyreflectivity may be more efficient for identifying vehicles whentaillamps or headlamps are off. Method 700 may include further steps.For example, method 700 may include determining a distance to theparticular object and classifying the particular object based on thedetermined distance and the spatial relationship between the firstportion and the at least two additional portions. For example, theobject may be classified as a vehicle if the distance to the particularobject is above one threshold and/or below another threshold. In certainaspects, the classification based on the spatial relationship and thereflectivity relationship may depend on the distance to the particularobject. For example, a particular spatial relationship and reflectivityrelationship may be classified as a vehicle only when the distance tothe object is above one threshold and/or below another threshold.

In another example, method 700 may include determining an angle of atleast one surface associated with the particular object and classifyingthe particular object further based on the determined angle and thereflectivity relationship between the first portion and the at least twoadditional portions. For example, the object may be classified as avehicle if the angle is above one threshold and/or below anotherthreshold. In certain aspects, the classification based on the spatialrelationship and the reflectivity relationship may depend on the angle.For example, a particular spatial relationship and reflectivityrelationship may be classified as a vehicle only when the angle is aboveone threshold and/or below another threshold. The one or more surfaceangles may be associated to the first, second or third portions of theparticular object, or to any other part thereof. The one or more surfaceangles may optionally be determined on a pixel-by-pixel basis by theLIDAR system.

In a third example, method 700 may include determining confidence levelsfor the detected/determined reflectivity of the first portion and/or forat least one of the at least two additional portions and classifying theparticular object further based on the one or more determined confidencelevels and the reflectivity relationship between the first portion andthe at least two additional portions. For example, the object may beclassified as a vehicle if one or more of the confidence levels areabove one threshold and/or below another threshold. In certain aspects,the classification based on the spatial relationship and thereflectivity relationship may depend on one or more of the confidencelevels. For example, a particular spatial relationship and reflectivityrelationship may be classified as a vehicle only when one or more of theconfidence levels are above one threshold and/or below anotherthreshold.

Optionally, method 700 may include determining a confidence level forthe determined reflectivity fingerprint and considering the determinedconfidence when classifying the particular object. For example, theobject may be classified only when the confidence level exceeds athreshold. Additionally or alternatively, the at least one processor mayidentify multiple possible classifications based on the determinedreflectivity fingerprint and select from the possible classificationsbased on confidence levels associated with the possible classifications.

In yet another example, method 700 may include determining distancesfrom the LIDAR system to the first portion and the at least twoadditional portions of the particular object and accounting fordetermined distances when classifying the particular object. Forexample, the at least one processor may consider an absolute differencebetween the determined distances, a scaled difference (e.g., twice,thrice, etc.) between the determined distances, or the like. In certainaspects, the classification based on the spatial relationship and thereflectivity relationship may depend on the determined distances. Forexample, a particular spatial relationship and reflectivity relationshipmay be classified as a vehicle only when the determined distances areabove one threshold, below another threshold, and/or differ by no moreor by no less than a threshold (e.g., no more than 20 cm, no more than 3times, no less than 50 m, no less than 2 times, etc.).

Optionally, step 709 may include classifying the object additionally oralternatively based on its relationship with other objects (or at leastother detections) in the FOV. For example, if the particular object islocated 15 m above the ground (or a road), it may not be classified as aterrestrial vehicle. In another example, if the particular object islocated between lane markings, the object may be classified as a car.

In embodiments using a variable-power LIDAR, upon classifying theparticular object as a vehicle, the at least one processor may directmore light in a subsequent scanning cycle towards the particular object.The at least one processor may direct more light only towards a regionof the field of view including the particular object or may direct morelight toward the field of view in general.

Optionally, method 700 may further include classifying at least oneadditional object as being an object other than a vehicle, based on adetermined reflectivity fingerprint of the at least one additionalobject. For example, the at least one additional object may beclassified as a street sign, a lamppost, a stoplight, a road marking, orthe like, depending on a reflectivity fingerprint (described above) ofthe at least one additional object. It is noted that the reflectivityfingerprints of other types of object may include more than threeportions whose reflectivity (and possibly additional parameter) match toa predefined pattern.

Similarly, method 700 may further include classifying a plurality ofobjects within the field of view and distinguishing between theplurality of objects based on a unique (or at least quasi-unique)reflectivity fingerprint associated with each of the plurality ofobjects. For example, the at least one processor may distinguish betweena street sign, a lamppost, a stoplight, a road marking, or the like,depending on reflectivity fingerprints associated therewith. Classifyingobjects other than vehicles based on associated reflectivityfingerprints may be implemented in addition to or instead of theclassification of other objects in the scene as vehicles.

As discussed above, in addition to classification, reflectivity may beincorporated into a point cloud map constructed based on LIDAR data. Thegeneration of a point cloud in which different reflectivity values(optionally adjusted for distance) may be implemented as part of method700 or independently. Accordingly, method 700 may further includeenabling construction of a 3D map of an environment around the vehicle,the 3D map including data representative of a reflectivity of objects inthe field of view. For example, the point cloud map of FIG. 1C mayincorporate reflectivity data in addition to (or in lieu of) determineddistances.

In some embodiments, the vehicle on which the LIDAR system is installed(also referred to below as “the host vehicle,” to avoid confusion withthe vehicle detected and classified by the LIDAR system) may be at leastpartially autonomous. Accordingly, method 700 may include causing achange in an operation of the host vehicle based on a type of theparticular object. For example, if the particular object is classifiedas a vehicle, the at least one processor may cause the host vehicle toslow down (by decelerating and/or by braking) and/or change lanes toavoid a collision with the particular object. In another example, if theparticular object is classified as a stop sign, the at least oneprocessor may cause the host vehicle to stop at the stop sign. In yetanother example, if the particular object is a road marking, the atleast one processor may cause a steering adjustment of the vehicle tokeep the vehicle within the road marking.

FIG. 8 depicts an example method 800 for classifying vehicles based on areflectivity fingerprint. Method 800 may be executed by at least oneprocessor, such as a processor of a LIDAR system (e.g., at least oneprocessor 118 of processing unit 108 in the example of FIG. 1A, twoprocessors 118 of processing unit 108 of FIG. 2A, any one or more ofprocessing units 108 of FIGS. 5A-5C, or the like). In some embodiments,method 800 may be executed separately from method 700. It is noted thatmethod 800 may be implemented by any type of LIDAR system, and notnecessarily by LIDAR system 100. For example, method 800 may be executedby a scanning or a non-scanning LIDAR system, by a pulsed orcontinuous-wave LIDAR system, or the like.

In other embodiments, method 800 may be executed in combination withmethod 700. For example, one or more of steps 801 through 807 may beexecuted after step 709 of method 700. In another example, one or moreof steps 801 through 807 may be executed in lieu of the execution ofsteps 705 through 709, and vice versa.

At step 801, the at least one processor determines a reflectivityfingerprint of a particular object based on the reflectivity of thedetected portions. As explained above, the reflectivity fingerprint mayinclude the reflectivities of the detected portions and may furtherinclude a spatial relationship between the detected portions and/orsizes of the detected portions.

At step 803, the at least one processor accesses memory that stores aplurality of indicators of fingerprints of various objects. For example,a database of reflectivity fingerprints with associated classificationsmay be accesses. The database may store entire fingerprints or may storeone or more unique features (i.e., indicators) of the fingerprints. Forexample, the database may store indicators indicative of spatialrelationship between locations associated with different reflectivities(e.g., a point with high reflectivity positioned substantially in themiddle between two points with lower reflectivity, where thereflectivity of the two points is substantially the same, and all threepoints are separated by areas lacking a detection).

At step 805, the at least one processor compares the reflectivityfingerprint of the particular object with the indicators of fingerprintsof various objects stored in memory to identify a match. In someembodiments, a match may comprise a reflectivity fingerprint that mapsonto a stored fingerprint (or indicators of the reflectivity fingerprintthat map onto stored indicators). In other embodiments, a match maycomprise a reflectivity fingerprint that partially maps onto a storedfingerprint (or indicators of the reflectivity fingerprint thatpartially map onto stored indicators). In such embodiments, the partialmapping may be considered a match only if above a certain threshold(e.g., at least 80% of the indicators match, at least 90% of thefingerprints match, etc.).

At step 807, the at least one processor determines a type of the vehiclebased on the identified match. For example, the at least one processormay determine that the vehicle is a minivan, a sport utility vehicle(SUV), a sedan, a semi-truck, or the like. In some embodiments, the atleast one processor may determine the type based on a single identifiedmatch. In other embodiments, the at least one processor may identifymultiple possible matches based on the reflectivity fingerprint andselect from the possible matches based on confidence levels associatedwith the possible matches.

Optionally, method 800 may further include classifying or determiningthe type of at least one additional object other than a vehicle, basedon a determined reflectivity fingerprint of the at least one additionalobject. For example, the at least one additional object may beclassified as a street sign, a lamppost, a stoplight, a road marking, orthe like, depending on a reflectivity fingerprint (described above) ofthe at least one additional object. It is noted that the reflectivityfingerprints of other types of object may include more than threeportions whose reflectivity (and/or additional parameters) match one ormore predefined patterns.

FIG. 9 depicts an example of identification performed using areflectivity fingerprint. For example, the identification depicted inFIG. 9 may be determined by executing method 700, method 800, or anycombination thereof. In the example of FIG. 9, two portions of lowreflectivity are located on either side of a portion with highreflectivity. Accordingly, the processor may classify the object as avehicle (e.g., by determining that the high reflectivity portion is alicense plate and the two portions are headlamps). Other portions of thevehicle may also have their reflectivity measured and encapsulated intothe reflectivity fingerprint of the vehicle. As exemplified in FIG. 9,each of the portions of the particular object of methods 700 and/or 800may include more than one pixel and/or more than one point-cloud point.

Although the above examples are described with respect to the use ofreflectivity fingerprints, the same embodiments may be employed withrespect to other data from the LIDAR system. For example, noise levels,ambient light levels, confidence levels, determined velocity (or otherchanges in positions and/or size of detected objects), changes insignals or other variables (such as those listed above) across frames,or any combination thereof may be used as fingerprints. In suchembodiments, this other data may also be incorporated into a point cloudmap with distance information from the LIDAR system. It is noted thatmethods 700 and 800 may be adapted for utilization of fingerprints basedon any combination of one or more of: noise levels, ambient lightlevels, confidence levels, determined velocity (or other changes inpositions and/or size of detected objects), changes in signals or othervariables (such as those listed above) across frames, either in additionto or in lieu of reflectivity, mutatis mutandis.

In one example, the processor may determine the least onedetection-quality value during the same scanning cycle in which thedetection information was collected by the detector. In such an example,the processor may output a point cloud model which includesdetection-quality value for most or all of the PC points, before afollowing scanning cycle is finished.

The detection-quality value assigned to each out of one or more pointsof the PC may be indicative of a likelihood that the point cloud modelcorresponds to a real-world object located at the respective locationidentified by the PC point, and is not an erroneous detection. Thedetection-quality value may be particularly useful when the detectedsignals did not result from reflection of an object in the respectivelocation inside the FOV (e.g., resulted from emission from the objectreceived by the detector). The detection-quality value is also referredto as “confidence level,” “detection likelihood”, “detectionprobability,” and “assurance level.”

In different examples, the confidence level may be provided in manydifferent types of data. e.g., high/low; high/medium/low, percent,integer, fraction, and so on. In embodiments where a few discreet levelsof confidence are used, different thresholds may be defined between thehigh and low conference of detection.

The processor (e.g., processor 118) may determine the detection-qualityvalue based on various parameters, either deriving from the detectionsignal from the respective direction associated with the respective PCpoint, from a detection signal of another direction in the FOV, fromsystem operational parameters (also referred to as “systemconfiguration,” such as illumination settings/parameters, detectorssettings/parameters, etc.), and so on. For example, the processor may becollectively configured to estimate a noise level parameter associatedwith the direction associated with the detection-quality value and todetermine the detection-quality value therefrom. The noise levelparameters may be, for example, a signal-to-noise ratio (SNR), anambient light signal level (e.g., photons/picosecond), system noiselevel (e.g., amplification noise), and so on.

The determination of the noise level and/or using the noise levelparameter for the determination of the detection-quality value may beperformed in response to the location of the respective point cloud datapoint (in the environment and/or in the FOV and/or in the point cloud).For example, if a high-energy signal is detected from a far location,the likelihood of the signal being noise may be relatively low (e.g.,assuming that noise sources are relatively uniformly located indifferent distances), and thus the signal is more likely a reflectionsignal from a highly reflective object.

System configuration may include a number of repetitions, a number ofpulses in a train, laser power, etc. For example, if a givenillumination energy toward a direction (e.g., a pixel) is projected in asingle burst, in several pulses or as a continuous wave, the noisebehavior (and therefore the likelihood of false detection for thatpixel) may change. For example, the detection-quality value may bedetermined based on one or more statistical analyses of reflections ofdifferent pulses to the same pixel.

Optionally, the processor may process the PC in view of the one or moredetection-quality values determined for different points in the pointcloud. For example, the at least one processor may use the determineddetection-quality values for detection of objects in the scene, forclassification of objects in the scene, for scene understanding, forestimating a state of the LIDAR system, for estimating an environmentalcondition for the LIDAR system, and so on. It should be noted that whilethe present disclosure refers mainly to point-cloud as the output 3Dmodel of the LIDAR, other types of 3D models, such as depth maps andpolygon meshes, may also be used.

It is noted that confidence value may be provided by the LIDAR system toany other system for additional processing. For example, confidencevalues may be provided together with PC, optionally as part of the PCand/or associated with different PC points. In certain aspects,processed confidence values may also be provided together with objectdetection/classification information. The inclusion of confidence valuesmay facilitate provisioning of more complex object information thanextant systems (e.g., indicating that an object is detected at a givenlocation, 99.8% likely a vehicle, 99.2% likely a car, 80% a car facingat direction 314°+3°).

Detecting Angles of Objects

In addition to determining distances based on light reflected fromobjects in the scene, LIDAR systems disclosed herein may furtherdetermine one or more surface angles of objects based on reflectedlight. In particular, various types of temporal distortions (e.g.,“stretching”) of reflected light pulses may be processed to obtain oneor more surface angles. The determined angles may be used for navigation(e.g., adjustments based on a detected angle of a road), for objectidentification or classification (e.g., using a “surface anglefingerprint”), and for various other types of decision making by theLIDAR system or its host (e.g., understanding of its surrounding,planning wave propagation in the space around it, etc.).

Embodiments of the present disclosure may further provide angleinformation regarding one or more detected objects. For example, one ormore surface angles of the identified object may be calculated. Inaddition to or in lieu of deriving surface angle from differences indistances to different pixels, embodiments of the present disclosure mayalso allow for determining a surface angle based on reflections signalsof a single pixel. The latter determination may be performed, forexample, based on a duration (or “temporal stretch”) of a return pulse,indicating one surface angle at the point of reflection. If the systemconstructs a point cloud map, a second surface angle at the point ofreflection may be determined based on the map, or in other ways such as(but not limited to) the ones discussed below. Additionally oralternatively, the latter determination may be performed based ondifferences in return time to different quadrants of one or moresensors. The different quadrants may be smaller than a SiPM (e.g., thequadrants do not belong to different pixels) but having locationssufficiently distinct to enable calculation of two surface angles at thepoint of reflection.

FIG. 10 depicts an example method 1000 for detecting an angularorientation of an object. Method 1000 may be executed by at least oneprocessor, such as a processor of a LIDAR system (e.g., at least oneprocessor 118 of processing unit 108 in the example of FIG. 1A, twoprocessors 118 of processing unit 108 of FIG. 2A, any one or more ofprocessing units 108 of FIGS. 5A-5C, or the like). It is noted thatmethod 1000 may be implemented by any type of LIDAR system, and notnecessarily by LIDAR system 100. For example, method 1000 may beexecuted by a scanning or a non-scanning LIDAR system, by a pulsed orcontinuous-wave LIDAR system, or the like.

At step 1001, the at least one processor controls at least one lightsource for illuminating a field of view. For example, the at least oneprocessor may activate the light source for deflection by a deflector ofthe light across the field of view.

In another example, the at least one processor may control the at leastone light source in a manner enabling light flux of light projected fromthe at least one light source to vary during scanning of the field ofview. In certain aspects, the at least one processor may vary the timingof pulses from the at least one light source. Alternatively orconcurrently, the at least one processor may vary the length of pulsesfrom the at least one light source. By way of further example, the atleast one processor may alternatively or concurrently vary a size (e.g.,length or width or otherwise alter a cross-sectional area) of pulsesfrom the at least one light source. In a yet further example, the atleast one processor may alternatively or concurrently vary the amplitudeand/or frequency of pulses from the at least one light source. Incertain aspects, the at least one processor may vary the light fluxduring a single scan and/or across a plurality of scans. Additionally oralternatively, the at least one processor may vary the light flux acrossa plurality of regions in the field of view (e.g., during a scan and/oracross a plurality of scans).

In other embodiments, method 1000 may be performed without varying thelight flux of the at least one light source. For example, method 1000may be performed with a LIDAR system that is fixed-power rather thanvariable-power.

At step 1003, the at least one processor scans a field of view bycontrolling movement of at least one deflector at which the at least onelight source is directed. For example, the at least one processor maycontrol movement of light deflector 114 of FIG. 1A, light deflectors 14Aand 114B of FIG. 2A, light deflectors 216 of FIG. 2B, light deflector216 of FIG. 2C, or the like. In some embodiments, step 1003 may beomitted from method 1000. For example, method 1000 may be performed witha LIDAR system that is stationary rather than scanning.

In some embodiments, method 1000 may further include controlling the atleast one light deflector such that during a scanning cycle of the fieldof view, the at least one light deflector instantaneously assumes aplurality of instantaneous positions. In one example, the at least oneprocessor may coordinate the at least one light deflector and the atleast one light source such that when the at least one light deflectorassumes a particular instantaneous position, a portion of a light beamis deflected by the at least one light deflector from the at least onelight source towards an object in the field of view, and reflections ofthe portion of the light beam from the object are deflected by the atleast one light deflector toward at least one sensor. In anotherexample, the at least one light source may comprise a plurality oflights sources aimed at the at least one light deflector, and the atleast one processor may control the at least one light deflector suchthat when the at least one light deflector assumes a particularinstantaneous position, light from the plurality of light sources isprojected towards a plurality of independent regions in the field ofview.

Optionally, the at least one processor may further control the at leastone light source in a manner enabling modulating the projected light anddistinguishing between light reflected from objects in the field of viewand light emitted by objects in the field of view. For example, the atleast one processor may pulse the at least one light source such thatthe gap between pulses is sufficiently long to receive light emitted byobjects in the field of view rather than light reflected from objects inthe field of view.

It is noted that steps 1001 and 1003 are optional, and may alternativelybe executed by another system than the system which utilizes thedetection results for determining of angular orientations. For example,steps 1001 and/or 1003 may be executed by a LIDAR sensing system (e.g.,located at the front of a vehicle) that may process the sensor detectionto provide a 3D model of the surrounding, and steps 1005, 1007, and 1009may be executed by a LIDAR system that may process these inputs todetermine angular orientations for surfaces of objects in the FOV, suchas a host system of the vehicle. It is noted that the system whichexecutes steps 1005, 1007, and 1009 may also utilize other sensor inputs(e.g., camera, radar, etc.) to analyze the environment of the vehicle inaddition to the LIDAR detection results. In embodiments where steps 1001and 1003 are executed by another system, method 1000 may alternativelybegin with receiving detection results from a LIDAR sensing systemincluding temporal distortion information associated with differentlocations in the environment of the LIDAR sensing system.

At step 1005, the at least one processor receives from at least onesensor, reflections signals indicative of light reflected from an objectin the field of view. For example, the at least one processor mayreceive the signals from sensor 116 of FIG. 1A or FIG. 2A, sensors 116of FIG. 2B, sensor 116 of FIG. 2C, or the like. In some embodiments, thesignals may comprise raw signal data from the at least one sensor.

In other embodiments, one or more processors (e.g., the at least oneprocessor and/or one or more other processors) may first perform one ormore operations on the raw signal data. For example, as discussed above,the one or more operations may include correction and/or normalizationbased on a detected distance for each signal. In another example, theone or more operations may include correction and/or normalization fornoise (e.g., based on an expected level of noise and/or based on anexpected signal level). In yet another example, the one or moreoperations may include application of one or more filters such as a lowpass filter, a bandpass filter, and/or a high pass filter.

Optionally, the at least one processor may additionally process thereflections signals to determine a single detection location andassociate the angular orientation (determined in step 1009) with thesingle detection location. For example, if the angular orientation iscalculated for a portion of the road, the at least one processor maydetermine a location for the portion of the road and associate theangular orientation therewith. In another example, if the angularorientation is calculated for another vehicle, the at least oneprocessor may determine a location for the other vehicle (or part of theother vehicle) and associate the angular orientation with the determinedlocation. Such an association may allow the at least one processor alsoto calculate a point cloud map including angular orientations associatedwith one or more points on the map. Accordingly, method 1100 may furtherinclude constructing (or at least enabling construction of) a 3D map ofthe field of view, the 3D map including data indicative of surfaceangular orientations of objects in the field of view. The angularorientations determined by the processor may correspond to planarsurfaces which are either tangent to a respective surface of the object,an average of a portion of the surface, or another estimation of thesurface of the object.

In certain aspects, the at least one processor may use additional datato determine the single detection location. For example, the at leastone processor may use data from other pulses, determine a peak of areturning pulse, etc. Additionally or alternatively, the receivedreflection signals that are associated with the single detectionlocation may be detected by a single pixel in the at least one sensor.For example, the at least one processor may use the reflections signalsdetected by the single pixel and reflections signals detected byadjacent pixels to determine two angles defining a normal of a surfaceof the object. In certain aspects, the single pixel may correspond to aplurality of detection elements grouped into a plurality ofnon-overlapping regions, and the at least one processor may determinethe temporal distortion by comparing information obtained fromreflection signals associated with the plurality of non-overlappingregions. It is noted that while these non-overlapping regions may becompletely non-overlapping (i.e., mutually exclusive), in someimplementations some overlap may be permitted.

Optionally, the reflection signals received from the at least one sensormay include at least three measurements indicative of time-of-flight ofthe light reflected from the object. In such embodiments, the at leastone processor may determine, based on differences in reception times ofthe at least three measurements, two angles defining a normal vector ofa surface of the at least the portion of the object. In certain aspects,the two angles may include a tilt angle and a roll angle of the surfaceof the at least the portion of the object. The determination of surfaceorientation parameters based on the difference in reception timings ofat least three measurements is discussed below in greater detail, e.g.,with respect to FIG. 12B.

At step 1007, the at least one processor detects at least one temporaldistortion in the reflections signals. As used herein, a “temporaldistortion” may refer to a stretching of the return pulse in time (e.g.,as compared to an illumination pulse). This temporal distortion may becaused by light returning from more distant parts of the surface laterthan from nearer parts (the difference in distances being, for example,a result of the surface inclination with respect to an optical axis ofthe LIDAR system). Additionally or alternatively, “temporal distortion”may refer to a difference in return time to different quadrants of theat least one sensor.

At step 1009, the at least one processor determines from the at leastone temporal distortion an angular orientation of at least a portion ofthe object. In some embodiments, the at least the portion of the objectmay correspond to a two-dimensional surface. For example, the at leastthe portion of the object may comprise a bumper of another vehicle, awindshield of another vehicle, a region of a road, a wall of a building,or the like. In some embodiments, the at least one processor maydetermine angular orientation of the object on a pixel by pixel basis.

Optionally, the at least one detected temporal distortion may beassociated with a plurality of reflections from the object. For example,a shape of a first temporal distortion associated with a first angularorientation may differ from a shape of a second temporal distortionassociated with a second angular orientation.

In embodiments where the object is an opposing vehicle, the angularorientation may be a slope of a portion of the opposing vehicle. Inembodiments where the object is a road, the angular orientation may be aslope of a portion of the road.

Method 1000 may further include additional steps. For example, method1000 may include causing a change in an operational state of the vehiclebased on the detected angular orientation of the portion of the road.For example, if road has inclining angle, the at least one processor maycause the vehicle to accelerate and/or to downshift. In another example,if road has declining angle, the at least one processor may cause thevehicle to decelerate, brake, and/or to downshift.

In another example, method 1000 may further include accessing memorythat stores a plurality of distortion patterns, each of the plurality ofdistortion patterns corresponding to a unique angular orientation, anddetermining the angular orientation by matching a stored distortionpattern with the at least one detected temporal distortion. For example,the at least one processor may determine a match similar to determininga match as described with respect to step 805 of method 800.Accordingly, method 1000 may include angular orientation determinationbased on a “distortion fingerprint,” similar to object classificationbased on a “reflectivity fingerprint,” as described above. The pluralityof distortion patterns may be stored in any form of analog or digitalmemory storage, or a combination of both. Comparison of reflectionsignals to distortion patterns stored in the form of matched filter, forexample, is discussed below in greater detail with respect to FIG. 14.

In yet another example, method 1000 may further include determiningdiffering angular orientations of a plurality of objects identified inan environment of the vehicle. For example, the at least one processormay determine an angular orientation of at least part of another vehicleand an angular orientation of at least part of a road.

In a similar example, method 1000 may further include determiningdiffering angular orientations associated with differing surfaces of theobject and identifying the object based on the determined angularorientations. For example, the at least one processor may identify theobject by matching stored angular orientations with the determinedangular orientations. In certain aspects, the at least one processor maydetermine a match similar to determining a match as described withrespect to step 805 of method 800. Accordingly, method 1000 may includeobject identification based on an “angular orientation fingerprint,”similar to object classification based on a “reflectivity fingerprint,”as described above.

Additionally or alternatively, any of the fingerprints described herein,such as the “angular orientation fingerprint,” “reflectivityfingerprint,” or the like may be run through one or more neural networksto determine a best match or a list of most likely matches (optionallywith a confidence level associated with each match in the list). Theneural network(s) may be trained using a training set of LIDARmeasurement results and/or may continue to increase in accuracy duringoperation, e.g., by adjusting to minimize one or more loss functions.

In embodiments where two angles are determined for differing surfaces,the at least one processor may classify the object at least in partbased on surface angular orientations of two surfaces of the object.Moreover, in embodiments where the vehicle is at least partiallyautonomous, the at least one processor may cause a change in anoperation of the vehicle based on surface angular orientation of thesurface of the object. For example, if the object is a barrier andangles toward the vehicle, the at least one processor may cause thevehicle to steer away from the barrier. In another example, if theobject is a road marking and angles away from the vehicle, the at leastone processor may cause the vehicle to steer towards the marking.Optionally, the processor may cause a change in an operation of thevehicle based on a change in the surface angular orientations of one ormore surfaces in the scene. For example, changes in the surfaceorientations of a remote car detectable only in very few pixels mayindicate that a car is turning and may further indicate its direction inturn. Based on such information, the at least one processor may decideto cause an acceleration, deceleration, and/or turning of the hostvehicle.

Optionally, the at least one processor may change an operationalparameter of the at least one light source based on the determinedangular orientation of the at least a portion of the object. Forexample, the at least one processor may cause increased light flux to bedirected to the at least a portion, e.g., in a next scan cycle.Additionally or alternatively, the at least one processor may change anoperational parameter of the at least one sensor based on the determinedangular orientation of the at least a portion of the object. Forexample, the at least one processor may cause the at least one sensor toincrease or decrease a detection threshold for a region of the field ofview including the at least a portion.

In another example, method 1000 may further include using a plurality ofmatched filters, each matched filter operable to correlate (a) one ormore temporal sequences of detected reflection levels and (b) areturn-signal hypothesis (also termed a “duration hypothesis”) toprovide a correlation output. An example of a LIDAR system having aplurality of filters is depicted in FIG. 14. Moreover, an example ofcorrelating reflection levels and return-signal hypotheses is depictedin FIG. 16. Optionally, the at least one processor may detect the atleast one temporal distortion in the reflections signals by correlatinga temporal sequence of detected reflection levels with a plurality ofreturn-signal hypotheses using the plurality of matched filters andselecting a closest match. Similarly, the at least one processor maydetect the at least one temporal distortion in the reflections signalsby correlating a temporal sequence of detected reflection levels with aplurality of return-signal hypotheses using one or more neural networkstrained to select a closest match (or to output a list of matchesoptionally including a confidence level for each match).

In embodiments where the object is a road, the at least one processormay process a matching of the temporal sequence of detected reflectionlevels and a shortest return-signal hypothesis and determine a presenceof a road sign marking on a portion of the road. In such embodiments,the portion of the road may be associated with a single pixel, and theat least one processor may determine two different distances for thesingle pixel. For example, the at least one processor may determine afirst distance to the portion of the road and a second distance to theroad sign marking on the portion of the road. For example, if areflectivity of the road marking is higher than that of the road, thenthe at least one processor may determine the distance to the roadmarking with higher accuracy that that determined for the portion of theroad (e.g., the at least one processor may determine a distance of 50meters to the portion of the road and determine a distance of 50.2 tothe road marking). Further details regarding the detection of lanemarking and other road sign marking are provided below.

In embodiments where method 1000 includes enabling construction of a 3Dmap of the field of view, method 1000 may further include outputting apoint cloud (or other 3D model) including different surface anglesassociated with different points of the model. Accordingly, method 1000may output the point cloud model in addition to or in lieu of one ormore determined angular orientations. In the point cloud, each PC point(out of some or all of the points of the PC) may include informationregarding surface angular orientation associated with that specific PCpoint. Optionally, the surface angular orientation associated with eachPC point may be determined by analyzing only the light arriving from thedirection of the PC point, without any additional detection informationand/or position of neighboring PC points.

It is noted that adjusting the detection of the LIDAR system to accountfor temporal distortions of the reflected signals (such as stretching ofthe reflected signal over time) may be used for other determinations inaddition to—or instead of—detection of angular orientations. Forexample, in noisy conditions, correlations between the emittedillumination pulse and the reflected signal may be determined in orderto detect parts of the detection signals resulting from actualreflection of the light pulse off an object in the scene rather thanfrom noise, ambient light, etc. However, if the reflected signal isstretched (e.g., slanting of the incidence surface, atmosphericconditions, etc.), a correlation intended to improve detection of actualreflection may—in such cases-hinder detection of the reflected signal.Utilization of a filters bank (e.g., as discussed below with respect toFIG. 14), correlating for a plurality of duration hypotheses, orotherwise accounting for such spreading may improve detectionresults—especially for remote and/or low-reflectivity targets. Inaddition to—or in lieu of—processing temporal distortions fordetermining inclination angles of surfaces, temporal distortions may beprocessed by the LIDAR system in order to assess atmospheric conditionsin the propagation path of light to and from an object in the scene.Such atmospheric conditions (also referred to as “volumetricconditions”) may be detected by the LIDAR system based on the signaltemporal distortion they cause may include, among others: fog, smoke,hot air, air turbulences, and the like. If an angular orientation of anobject in the scene is known in advance (e.g., a wall of a buildingknown based on a preexisting map), the temporal distortions may bedivided by the LIDAR system between the different causes—in thisexample, orientation of surface and volumetric conditions.

FIG. 11 depicts an example method 1100 for detecting road-surfacemarkings on roads. Method 1100 may be executed by at least oneprocessor, such as a processor of a LIDAR system (e.g., at least oneprocessor 118 of processing unit 108 in the example of FIG. 1A, twoprocessors 118 of processing unit 108 of FIG. 2A, any one or more ofprocessing units 108 of FIGS. 5A-5C, or the like). It is noted thatmethod 1100 may be implemented by any type of LIDAR system, and notnecessarily by LIDAR system 100. For example, method 1100 may beexecuted by a scanning or a non-scanning LIDAR system, by a pulsed orcontinuous-wave LIDAR system, or the like.

At step 1101, the at least one processor controls at least one lightsource for illuminating a field of view. For example, the at least oneprocessor may activate the light source for deflection by a deflector ofthe light across the field of view.

In another example, the at least one processor may control the at leastone light source in a manner enabling light flux of light projected fromthe at least one light source to vary during scanning of the field ofview. In certain aspects, the at least one processor may vary the timingof pulses from the at least one light source. Alternatively orconcurrently, the at least one processor may vary the length of pulsesfrom the at least one light source. By way of further example, the atleast one processor may alternatively or concurrently vary a size (e.g.,length or width or otherwise alter a cross-sectional area) of pulsesfrom the at least one light source. In a yet further example, the atleast one processor may alternatively or concurrently vary the amplitudeand/or frequency of pulses from the at least one light source. Incertain aspects, the at least one processor may vary the light fluxduring a single scan and/or across a plurality of scans. Additionally oralternatively, the at least one processor may vary the light flux acrossa plurality of regions in the field of view (e.g., during a scan and/oracross a plurality of scans).

In other embodiments, method 1100 may be performed without varying thelight flux of the at least one light source. For example, method 1100may be performed with a LIDAR system that is fixed-power rather thanvariable-power.

At step 1103, the at least one processor scans a field of view bycontrolling movement of at least one deflector at which the at least onelight source is directed. For example, the at least one processor maycontrol movement of light deflector 114 of FIG. 1A, light deflectors114A and 114B of FIG. 2A, light deflectors 216 of FIG. 2B, lightdeflector 216 of FIG. 2C, or the like. In some embodiments, step 1003may be omitted from method 1000. For example, method 1000 may beperformed with a LIDAR system that is stationary rather than scanning.

In some embodiments, method 1100 may further include controlling the atleast one light deflector such that during a scanning cycle of the fieldof view, the at least one light deflector instantaneously assumes aplurality of instantaneous positions. In one example, the at least oneprocessor may coordinate the at least one light deflector and the atleast one light source such that when the at least one light deflectorassumes a particular instantaneous position, a portion of a light beamis deflected by the at least one light deflector from the at least onelight source towards an object in the field of view, and reflections ofthe portion of the light beam from the object are deflected by the atleast one light deflector toward at least one sensor. In anotherexample, the at least one light source may comprise a plurality oflights sources aimed at the at least one light deflector, and the atleast one processor may control the at least one light deflector suchthat when the at least one light deflector assumes a particularinstantaneous position, light from the plurality of light sources isprojected towards a plurality of independent regions in the field ofview.

In some embodiments, the at least one processor may further control theat least one light source in a manner enabling modulating the projectedlight and to distinguish between light reflected from objects in thefield of view and light emitted by objects in the field of view. Forexample, the at least one processor may pulse the at least one lightsource such that the gap between pulses is sufficiently long to receivelight emitted by objects in the field of view rather than lightreflected from objects in the field of view.

At step 1105, the at least one processor receive from at least onesensor a reflection signal indicative of light reflected from a portionof a road, wherein the reflection signal spans a first duration (e.g.,above a threshold) and comprising a narrow peak having a maximalamplitude which is at least twice the maximal amplitude of the rest ofthe reflection signal, wherein the narrow peak spans a second durationwhich is at least 5 times shorter than the first duration. An example ofsuch a reflection signal is depicted in FIG. 15. The spanning (orstretching) of the reflection signal for a relatively long spans resultsfrom the road being viewed at relatively acute angle. The peak has muchhigher amplitude that the rest of the signal because the road markingwhich cause the peak reflection is much more reflective than the road.The peak is much shorter in time because it is narrow with respect tothe road area covered in the respective pixel.

At step 1107, the at least one processor determines an angularorientation of the road based on a temporal distortion in the reflectionsignal. For example, the at least one processor may compare the firstduration to the original pulse width and determine the angularorientation based on the comparison. An example of this determination isdepicted in FIG. 12A. Referring to the example of FIG. 14, thiscomparison may be made by matched filters of filters bank 1446 (e.g.,filters 1446(1) through 1446(6)).

In some embodiments, step 1107 may further include correlating thetemporal reflection data to each reference reflection pattern out of aplurality of reference reflection patterns (also termed “return-signalhypotheses” or “duration hypotheses”) of different durations, theplurality of reference reflection patterns including a first referencereflection pattern of a first duration and a second reference reflectionpattern of a second duration, longer than the first duration.Accordingly, step 1107 may be performed similarly to the optionalcorrelation of method 1000 (described above). An example of suchcorrelation is depicted in FIG. 16.

At step 1109, the at least one processor determines existence of aroad-surface marking on the portion of the road based on the narrowpeak. For example, the at least one processor may determine that a lanemarking and/or a reflector is located at a position from which thenarrow peak originated. Such a determination may be possible because,even though a ground-pixel-size for the road may be rather large, thewidth of the road-surface marking may be relatively constant and partialto the larger pixel, meaning the reflection for the road-surface markingmay not stretch over time as other parts of the pixel may. Accordingly,a difference in reflectivity between the road and the road-surfacemarking may be relatively large.

In some embodiments, an estimate of the distance for the road may beskewed if the reflective pulse for the road is skewed on account of thehigh reflectivity of the road-surface marking. In such embodiments, theat least one processor may correct distance estimates for the road usingthe road-surface marking, e.g., by using a more accurate (i.e., notskewed) distance estimate for the road-surface marking.

Method 1100 may further include additional steps. For example, method1100 may include determining a first distance to the portion of the roadand determining a second distance, different than the first distance,for the road sign marking on the portion of the road.

FIG. 12A illustrates an example of determining a surface angle based onstretching of a returning pulse in time. As illustrated in FIG. 12A, thewidth of a return pulse (whether absolutely or relative to therespective illumination pulse issued by the LIDAR system) may be used todetermine a surface angle of an object from which the returning pulseoriginated. In the example of FIG. 12A, the top return pulse may resultin a surface angle of 20° while the bottom return pulse, which islonger, may result in a surface angle of 50°. One of ordinary skill inthe art would understand that FIG. 12A is not to scale and is exemplaryand explanatory only rather than depicting a specific relation betweenpulse width and surface angle.

FIG. 12B illustrates an example of determining surface angles based ondifferences in return time to different quadrants of the sensors. In theexample of FIG. 12B, pixel 1210 is divided into four quadrants, e.g.,quadrants 1214A, 1214B, 1214C, and 1214D. Based on differing returntimes to different quadrants (e.g., return times T1, T2, T3, and T4),the processor may determine one or more surface angles for a surface ofthe object which is represented in three or more of the quadrants. Theone or more surface angles may be associated by the processor to thepoint-cloud point including location information determined based onreflection information detected in two or more of these quadrants.

FIG. 13A illustrates an example of angular orientations being slopes ofa portion of a vehicle. Similarly, FIG. 13B illustrates an example ofangular orientations being slopes of a portion of a road. Referring tothe representation of the one or more surface angles determined by theLIDAR system, it is noted that different methods of representation maybe used. For example, angular orientation information may be stored asangle information (with respect to the optical axis of the LIDAR system,with respect to an accepted reference coordinate systems such as GPSsystem, or with respect to other reference axes) indicative of theorientation of the surface, of a tangent to the surface, or a normal tothe surface; as a vector information (e.g., conforming with any one ofthe aforementioned coordinate systems); or the like.

FIG. 14 illustrates an example of a LIDAR system 1400 having a pluralityof filters 1446. In the example of FIG. 14, system 1400 includes anemitter 1410 (e.g., similar to emitter 112 of FIG. 2A). System 1400further includes a deflector 1430 (e.g., similar to deflector 216 ofFIG. 2C), and a plurality of sensors 1442 (e.g., similar to sensors 116of FIG. 2B). In the example of FIG. 14, the output of all the sensors1442 are processed by the same group of matched filters (e.g.sequentially). In an alternative embodiment (not shown), the outputs ofeach sensor 1442 (denoted 1444(1), 1444(2), and 1444(3)) may beprocessed by an independent group of matched filters (denoted 1446(1)through 1446(6)). A combination of those two embodiments may also beused (e.g., each group of matched filters 14460 may process the outputsof a subgroup of pixels). The number of matched filters 14460 used forprocessing the sensor detection signal of each of sensors 1442 maydiffer. In FIG. 14, a group of 6 matched filters is illustrated, butother numbers of matched filters may be implemented. Optionally, LIDARsystem 1400 may be an embodiment of LIDAR system 100, and any aspect ofLIDAR system 100 discussed in the present document may apply, mutatismutandis, to LIDAR system 1400. Matching filters (such as those of FIG.14) may also improve detection of the LIDAR system (e.g., providingbetter SNR, etc.).

Referring to the example of detecting line marking using temporalanalysis of the reflected signals and with respect to the example ofFIG. 14, an optional “MAX” filter may be added to filter bank 1446. The“MAX” filter may select the matched filter with the highest outcome ofthe correlation to reference pulses of varying durations. Accordingly,by selecting the max-peak time, the “MAX” filter may detect the locationof the lane marking, which may otherwise be undetected on account of thenarrowness of the peak. In some embodiments, a confidence score may beadded to the output of the “MAX” filter such that a lane marking is onlydetected when the confidence score exceeds a threshold.

An example of the operation of the filters of FIG. 14 is depicted inFIG. 16, described below. In particular, each filter may correlate thereflected signals with a return-signal hypothesis and then output thecorrelation.

FIG. 15 illustrates an example of a reflection signal spanning a firstduration and having a narrow peak. As depicted in Example 1 of FIG. 15,peak S3 may be taller and narrower than the remainder (e.g., S1 and S2)of the reflection signal. Accordingly, in some embodiments, S3 may bedetected as a lane marking. As depicted in Example 2, of FIG. 15, one ormore thresholds may be implemented for detecting a lane marking. InExample 2, peak S3 is five times shorter than the first pulse in thereflection signal and is four times as tall as the maximum height of theremainder of the reflection signal. Other thresholds, such as at leastfour times shorter than the shorter pulse in the remainder of thereflection signal, at least twice as tall as the maximum height of theremainder of the reflection signal, etc.

FIG. 16 illustrates an example of correlating temporal sequences ofdetected reflection levels and a return-signal hypothesis to provide acorrelation output. Although the example of FIG. 16 is depicted as usingLIDAR system 1400 of FIG. 14, any other LIDAR system may be used. Forexample, an embodiment of LIDAR system 100 may be used.

As can be seen in the example of FIG. 16, the signals returning fromtarget 1 and target 3 (denoted “S1” and “S3”) respectively aresignificantly more spread in time than the signal returning from target2 (denoted “S2”). Therefore, the energy of signals S1 and S3 is spreadover longer duration, which reduces their SNR.

As further illustrated in the example of FIG. 16, the detected signal(denoted “DS”) includes some noise is correlated with two return-signalhypotheses: hypothesis H1, in which the reflected signal is about thesame duration as the original signal (the “photonic inspection pulse”),and hypothesis H2, in which the reflected signal is temporally longer(e.g., “stretched”) with respect to the original signal. Moreover,expected power in hypothesis H2 is lower than that of hypothesis H1, atleast in part due to the stretching (e.g., because the same energy isspread over a longer time).

The correlation results in the example of FIG. 16 are convolutions ofthe detected signal with the respective duration hypothesis (denoted C1,C2). However, other mathematical operations (such as summing,subtracting, multiplying, or inputting into one or more functions) maybe applied additionally or alternatively. In the example of FIG. 16, thereflection from the object further from the LIDAR (i.e., target 2)results in a peak in convolution C1 (of the detected signal withhypothesis H1) higher than the peak in the same temporal location of theconvolution of the detected signal with convolution H2. Meanwhile, inthe temporal locations corresponding to photons reflected back fromtargets 1 and 3, convolution C2 yields higher peaks than thecorresponding times of convolution C1. Accordingly, as shown in theexample of FIG. 16, correlations of the return signal may be used toidentify temporal distortions by amplifying the distortions. Moreover,objects (such as lane markings) may be detected and/or identified basedon outputs of the correlations. For example, particular outputs (e.g.,above certain thresholds, matching certain fingerprints, etc.) may beassociated with an identification or classification that may be appliedto the object.

Classifying Objects with Additional Measurements

In addition to (or in lieu of) determining surface angles andreflectivities based on reflected pulses, LIDAR systems disclosed hereinmay further determine additional measurements, such as object surfacecomposition (e.g., whether metallic, whether including one or moreacrylics, etc.) or ambient illumination (e.g., pulses received that arenot reflected pulses). In addition, difference between measurements fromone frame to another may be used in navigation and objectclassification. Moreover, any measured value may be assigned acalculated confidence value, as discussed in greater detail below.

As discussed previously, systems of the present disclosure may providemore detailed feedback than extant systems. For example, LIDAR systemsof the present disclosure may output reflectivity (discussed above),surface angles (discussed above), confidence levels (discussed below),ambient light level, properties of detected objects (e.g., whether theobject is metal), or the like. In addition, LIDAR systems of the presentdisclosure may allow for determination of changes in any of the outputsas compared with one or more previous frames (e.g., changes inreflectivity, changes in confidence levels, changes in ambient light, orthe like). In some embodiments, this additional information may be useddirectly to detect objects and determine properties of detected objects.Additionally or alternatively, this additional information may be usedto construct and/or analyze point cloud maps (an example of which isdescribed above with respect to FIG. 1C).

In some embodiments, objects detected by a LIDAR system may beclassified based on confidence levels associated with one or moremeasurements (e.g., reflectivities, surface angles, surface composition,or the like). By identifying objects using confidence levels, objectsmay be classified with greater accuracy than extant LIDAR identificationtechniques. In addition, systems of the present disclosure may controlthe LIDAR to direct more light flux towards objects with less confidentclassifications than objects with more confident classifications,ensuring that power is more efficiently distributed.

Embodiments of the present disclosure that incorporate confidence levelsmay be particularly advantageous for noisy LIDAR systems and/orenvironments. Nonetheless, some advantages may be inherent on account ofinherent noisiness in the physics and statistics of reflections offew-photons LIDARs.

In embodiments where a point-cloud map is generated, the systems andmethods may also rank the different points of the model using confidencelevels. In one example, the systems and methods may incorporate theconfidence levels for the order of processing the model (e.g., creatinganchors in the model based on high confidence (e.g., above a threshold)points, apportioning computational resources to points or areas of highconfidence (e.g., above a threshold), etc.).

FIG. 17 depicts an example method 1700 for classifying objects insurroundings of a vehicle. Method 1700 may be executed by at least oneprocessor, such as a processor of a LIDAR system (e.g., at least oneprocessor 118 of processing unit 108 in the example of FIG. 1A, twoprocessors 118 of processing unit 108 of FIG. 2A, any one or more ofprocessing units 108 of FIGS. 5A-5C, or the like). It is noted thatmethod 1700 may be implemented by any type of LIDAR system, and notnecessarily by LIDAR system 100. For example, method 1700 may beexecuted by a scanning or a non-scanning LIDAR system, by a pulsed orcontinuous-wave LIDAR system, or the like.

At step 1701, the at least one processor receives on a pixel-by-pixelbasis, a plurality of measurements associated with LIDAR detectionresults, the measurements including at least one of: a presenceindication, a surface angle, object surface physical composition, and areflectivity level. For example, a “presence indication” may include anyindication of the presence of an object (e.g., the presence of non-zeroreflection) or distance information to a detected object as measured bythe LIDAR. A “surface physical composition” may include one or moreindicators of the material composition of an object in the field ofview. For example, whether the object is metallic, which metals areincluded in a metallic alloy, which type (water-based or oil-based) ofpaint is on an object, or the like may be determined using, e.g., thepolarity of the detections. The terms “surface angle” and “reflectivitylevel” are used consistently with the foregoing description. In additionto—or in lieu of—surface physical composition, physical characteristicsof deeper layers of the object may also be determined, e.g. transparencylevels, crystal characteristics, etc.

At step 1703, the at least one processor receives on the pixel-by-pixelbasis, at least one confidence level associated with each receivedmeasurement. The confidence level may include the confidence ofdetection (e.g., the confidence that an object is present, theconfidence of the detected signal itself, or the like), the confidenceof a surface angle (e.g., the confidence that the angle is correct, theconfidence in the calculated angle in view of the level of reflecteddata received, or the like), etc.

In some embodiments, the received measurements may include more than oneresult per pixel and/or have pixels with missing information.Additionally or alternatively, the confidence level of the receivedmeasurements may be associated with points of a point-cloud maps ratherthan pixels. For example, several pixels may be associated with a singlepoint (thus resulting in one confidence level for multiple pixels).

At step 1705, the at least one processor accesses classificationinformation for classifying a plurality of objects. For example, theclassification information may be stored on one or more memories. In oneexample, the classification information may comprise one or morefingerprints based on the one or more measurements and/or confidencelevels.

At step 1707, based on the classification information and the receivedmeasurements with the at least one associated confidence level, the atleast one processor may identify a plurality of pixels as beingassociated with a particular object. As explained above, the at leastone processor may identify the particular object using matching with theaccessed classification information (e.g., similar to the matching ofstep 805 of method 800, described above). Additionally or alternatively,steps 1705 and 1707 may be performed with one or more neural networkssuch that the at least one processor may use the one or more neuralnetworks to determine the best match (or a list of matches optionallyincluding a confidence level for each match).

As used herein. “classifying” or “identifying” broadly refers todetermining an existence of the particular object (e.g., an object mayexist in a certain direction with respect to the LIDAR system and/or toanother reference location and/or an object may exist in a certainspatial volume). Additionally or alternatively, the “classifying” or“identifying” may refer to determining a distance between the object andanother location (e.g., a location of the LIDAR system, a location onearth, or a location of another object). Additionally or alternatively,“classifying” or “identifying” broadly may refer to determining a typeof object such as car, plant, tree, road; recognizing a specific object(e.g., the Washington Monument); determining a license plate number;determining a composition of an object (e.g., solid, liquid,transparent, semitransparent); determining a kinematic parameter of anobject (e.g., whether it is moving, its velocity, its movementdirection, expansion of the object); or the like. Additionally oralternatively, “classifying” or “identifying” may include generating apoint cloud map in which every point of one or more points of the pointcloud map correspond to a location in the object or a location on a facethereof. In one example, the data resolution associated with the pointcloud map representation of the field of view may be associated with0.1°×0.1° or 0.3°×0.3° of the field of view.

As used herein, a “particular object” may refer to a single object (suchas a stop sign), a complex object formed of multiple sub-objects (suchas a car, formed of a bumper, license plate, headlights, windshield,etc.), or an identifiable portion of an object (such as a road markingon a road or a license plate on a car, etc.). Although described abovewith respect to object classification, method 1700 may additionally oralternatively be used in clustering, determining boundaries of an object(e.g., as depicted in the example of FIG. 22), or the like.

FIG. 18 depicts another example method 1800 for classifying objects insurroundings of a vehicle. Method 1800 may be executed by at least oneprocessor, such as a processor of a LIDAR system (e.g., at least oneprocessor 118 of processing unit 108 in the example of FIG. 1A, twoprocessors 118 of processing unit 108 of FIG. 2A, any one or more ofprocessing units 108 of FIGS. 5A-5C, or the like). In some embodiments,method 1800 may be executed separately from method 1700. It is notedthat method 1800 may be implemented by any type of LIDAR system, and notnecessarily by LIDAR system 100. For example, method 1800 may beexecuted by a scanning or a non-scanning LIDAR system, by a pulsed orcontinuous-wave LIDAR system, or the like.

In other embodiments, method 1800 may be executed in combination withmethod 1700. For example, one or more of steps 1801 through 1805 may beexecuted after step 1705 or step 1707 of method 1700.

At step 1801, the at least one processor receives a plurality ofmeasurements including a plurality of presence indications from multipleportions of the particular object. Additionally or alternatively, the atleast one processor may receive a plurality of measurements including aplurality of surface angles from multiple portions of the particularobject. Additionally or alternatively, the at least one processor mayreceive a plurality of measurements including a plurality ofreflectivity levels from multiple portions of the particular object.

At step 1803, the at least one processor determines, using the pluralityof presence indications and at least one confidence level associatedwith the plurality of presence indications, a shape of the object. Forexample, the at least one processor may determine the boundary (andhence the shape) of an object based on an outline of presenceindications with high (e.g., above a threshold) confidence levels.Additionally or alternatively, the at least one processor may determine,using the plurality of surface angles and at least one confidence levelassociated with the plurality of surface angles, a spatial relationshipbetween the multiple portions of the particular object. For example, theat least one processor may estimate one or more contours of the object(optionally mapping such contours using a point-cloud map) based onsurface angles with high (e.g., above a threshold) confidence levels.Additionally or alternatively, the at least one processor may determine,using the differing reflectivity levels, a reflectivity relationshipbetween the multiple portions of the particular object. For example, theat least one processor may determine a reflectivity fingerprint usingmethod 700, method 800, or a combination thereof.

At step 1805, the at least one processor classifies the particularobject based at least in part on the shape of the particular object,classifies the particular object based at least in part on thedetermined spatial relationship, and/or classifies the particular objectbased at least in part on the determined reflectivity relationship. Forexample, step 1805 may be performed similarly to step 1707 of method1700, described above.

FIG. 19 depicts yet another example method 1900 for classifying objectsin surroundings of a vehicle. Method 1900 may be executed by at leastone processor, such as a processor of a LIDAR system (e.g., at least oneprocessor 118 of processing unit 108 in the example of FIG. 1A, twoprocessors 118 of processing unit 108 of FIG. 2A, any one or more ofprocessing units 108 of FIGS. 5A-5C, or the like). In some embodiments,method 1900 may be executed separately from method 1700 and from method1800. It is noted that method 1900 may be implemented by any type ofLIDAR system, and not necessarily by LIDAR system 100. For example,method 1900 may be executed by a scanning or a non-scanning LIDARsystem, by a pulsed or continuous-wave LIDAR system, or the like.

In other embodiments, method 1900 may be executed in combination withmethod 1700 and/or method 1800. For example, one or more of steps 1901and 1903 may be executed after step 1705 or step 1707 of method 1700 orafter step 1803 or step 1805 of method 1800.

At step 1901, the at least one processor obtains an additionalmeasurement associated with a velocity of the particular object.Additionally or alternatively, the at least one processor may obtain anadditional measurement of ambient light and at least one confidencelevel associated with the ambient light associated with the particularobject.

At step 1903, the at least one processor classifies the particularobject based at least in part on the obtained measurement associatedwith a velocity of the particular object and/or classifies theparticular object based at least in part on the obtained measurement ofambient light associated with the particular object. For example, step1903 may be performed similarly to step 1707 of method 1700, describedabove.

Step 1707 of method 1700, step 1805 of method 1800, and step 1903 ofmethod 1900 may be combined in any appropriate manner. For example, theat least one processor may undertake the classification based onreceived measurements with the at least one associated confidence levelin combination with any derived measurements, such as a shape of theparticular object, a spatial relationship of the particular object, areflectivity relationship of the particular object, a velocity of theparticular object, ambient light associated with the particular object,or the like. Accordingly, the at least one processor may use a pluralityof cascading classifiers on the received measurements, confidencelevels, and/or derived measurements to perform the classification.Additionally or alternatively, the at least one processor may determinematches between the received measurements, confidence levels, and/orderived measurements and one or more known fingerprints (e.g., stored ina classification database).

Any of methods 1700, 1800, or 1900 may further include receiving aplurality of measurements including at least one presence indication, atleast one surface angle, and at least one reflectivity level frommultiple portions of the particular object and classifying theparticular object based on the plurality of measurements. Accordingly,as explained above, a plurality of received measurements may be used incombination with confidence levels (and/or with one or more derivedmeasurements) to perform the classification.

In some embodiments, any of methods 1700, 1800, or 1900 may furtherinclude determining a type of the particular object as at least one of:a vehicle, a car, a truck, a bus, a pedestrian, a building obstacle, acyclist, a motorcycle, a traffic sign, a building, a tunnel, a bridge, atree, an animal, and a hill.

In any of the embodiments described above, any of methods 1700, 1800, or1900 may further include causing a change in an operational state of thevehicle based on a relative location and the determined type of theclassified particular object. For example, if the object is classifiedas a rock or other hazard ahead of the vehicle, the at least oneprocessor may cause the vehicle to shift around the hazard. In anotherexample, if the object is classified as another vehicle ahead of thevehicle, the at least one processor may cause the vehicle to decelerateor brake.

Additionally or alternatively, any of methods 1700, 1800, or 1900 mayfurther include causing a change of an operational parameter of a lightsource associated with the LIDAR based on the determined type of theparticular object. For example, the at least one processor may causeincreased light flux to be directed to the particular object, e.g., in anext scan cycle, when classified as another vehicle. In another example,the at least one processor may cause decreased light flux to be directedto the particular object, e.g., in a next scan cycle, when classified asa pedestrian. Additionally or alternatively, any of methods 1700, 1800,or 1900 may further include causing a change of an operational parameterof a sensor associated with the LIDAR based on the determined type ofthe particular object. For example, the at least one processor may causethe sensor to increase a detection threshold for a region of the fieldof view including the particular object classified as a street lamp. Inanother example, the at least one processor may cause the sensor todecrease a detection threshold for a region of the field of viewincluding the particular object classified as a road.

FIG. 20 depicts an example method 2000 for classifying objects insurroundings of a vehicle. Method 2000 may be executed by at least oneprocessor, such as a processor of a LIDAR system (e.g., at least oneprocessor 118 of processing unit 108 in the example of FIG. 1A, twoprocessors 118 of processing unit 108 of FIG. 2A, any one or more ofprocessing units 108 of FIGS. 5A-5C, or the like). It is noted thatmethod 2000 may be implemented by any type of LIDAR system, and notnecessarily by LIDAR system 100. For example, method 2000 may beexecuted by a scanning or a non-scanning LIDAR system, by a pulsed orcontinuous-wave LIDAR system, or the like.

At step 2001, the at least one processor receives point-cloudinformation originating from a LIDAR system configured to project lighttoward the vehicle's surroundings. The point-cloud information may beassociated with a plurality of data points, and each data point mayinclude indications of a three-dimensional location and angularinformation with respect to a reference plane.

As discussed above, the point-cloud information may include apoint-cloud map (or a subset thereof) although the information may bereceived by the at least one processor before or after being used toconstruct a point-cloud map. For any point-cloud map described herein,each point may be associated with location information of different datapoints, which may be given in different forms, e.g. (X,Y,Z); (R,θ,ϕ);(R, pixel identification); etc.

In some embodiments, the received point cloud information may furtherinclude a confidence level for each of the measurements.

At step 2003, the at least one processor constructs, from the receivedpoint cloud information, a point cloud map of the vehicle'ssurroundings, wherein the point cloud map is indicative of a shape of aparticular object in the vehicle's surroundings and of angularorientations of at least two surfaces of the particular object. In someembodiments, the point cloud map may be further indicative of areflectivity level of different portions of the particular object.Additionally or alternatively, each pixel data point in the point cloudmap may include angular information with respect to a reference plane.In one example, the reference plane may include a road surface or theplane of the field of view. In some embodiments, each data point in thepoint cloud map may include measurements of at least two of: a presenceindication, a surface angle, a reflectivity level, velocity, and ambientlight.

At step 2005, the at least one processor accesses object-relatedclassification information. For example, the classification informationmay be stored on one or more memories.

At step 2007, the at least one processor identifies the particularobject based on the information from the point cloud map and theobject-related classification information. For example, step 2105 may beperformed similar to step 1707 of method 1700, step 1805 of method 1800,step 1903 of method 1900, or any combination thereof.

In some embodiments, step 2007 may include classifying a plurality ofpixels as being associated with the particular object. Additionally ofalternatively, identifying the particular object may include determininga type of the particular object.

Method 2000 may further include additional steps. For example, method2000 may further include determining a match by identifying a mostlikely three-dimensional representation in the classificationinformation that corresponds to the information in the point cloud map.In one example, determining a match may be performed by one or moreclassifiers (either individual or cascading). Additionally oralternatively, determining a match may be performed similar todetermining a match as described with respect to step 805 of method 800.Accordingly, method 2000 may include object identification based on afingerprint of composite measurements included in a point cloud mapsimilar to object classification based on an “angular orientationfingerprint” and/or a “reflectivity fingerprint.” as described above.

In another example, method 2000 may further include determining that theidentified particular object is an object of interest and forwarding theLIDAR an indication of a region of interest that includes the identifiedparticular object. An object of interest may include an object thataffects navigation of the vehicle, such as a road marking, anothervehicle, a road sign, a road hazard, or the like. Objects not ofinterest may include lamp posts, storefront signs, or the like. Theregion of interest may be forwarded to the LIDAR so that, for example,the LIDAR may cause a change of an operational parameter of a lightsource associated with the LIDAR for the region of interest based on theidentification of the particular object, cause a change of anoperational parameter of a sensor associated with the LIDAR for theregion of interest based on the identification of the particular object,or any combination thereof.

Additionally or alternatively, when a certainty level (e.g., associatedwith the identification) is under a threshold, method 2000 may includerequest additional information from the LIDAR. For example, the at leastone processor may request previously recorded information of the lastframe to improve the identification, request a change in an operationalparameter (e.g., associated with a light source, associated with asensor) in the next frame, or a combination thereof.

Accordingly, method 2000 depicts an example of using a “point cloudfingerprint” that combines distance information with one or moreparameters to identify and classify objects. A “point cloud fingerprint”may refer to a subset of points in a point cloud map along with anyassociated parameters (such as reflectivity, surface angle(s), etc.).The “point cloud fingerprint” of method 2000 may be matched similar toother fingerprints disclosed herein (such as a “reflectivityfingerprint” or a “surface angle fingerprint”).

FIG. 21 depicts an example method 2100 for classifying objects insurroundings of a vehicle. Method 2100 may be executed by at least oneprocessor, such as a processor of a LIDAR system (e.g., at least oneprocessor 118 of processing unit 108 in the example of FIG. 1A, twoprocessors 118 of processing unit 108 of FIG. 2A, any one or more ofprocessing units 108 of FIGS. 5A-5C, or the like). It is noted thatmethod 2100 may be implemented by any type of LIDAR system, and notnecessarily by LIDAR system 100. For example, method 2100 may beexecuted by a scanning or a non-scanning LIDAR system, by a pulsed orcontinuous-wave LIDAR system, or the like.

At step 2101, the at least one processor receives a plurality ofdetection results associated with LIDAR detection results, eachdetection result including location information, and further informationindicative of at least two of the following detection characteristics:object surface reflectivity; object surface orientation; temporaldistortion of a signal reflected from the object (e.g., spreading of thesignal); object surface physical composition; ambient illuminationmeasured at a LIDAR dead time; difference in detection information froma previous frame; and confidence level associated with another detectioncharacteristic provided for the detection results (e.g., confidencelevel associated with object surface physical composition).

In some embodiments, the detection results may include additionalinformation. For example, each detection result may include locationinformation, and further information indicative of at least three of thefollowing detection characteristics: object surface reflectivity; objectsurface orientation; temporal distortion of a signal reflected from theobject (e.g., spreading of the signal); object surface physicalcomposition; ambient illumination measured at a LIDAR dead time;difference in detection information from a previous frame; and one ormore confidence levels, each associated with another detectioncharacteristic provided for the detection results (e.g., confidencelevel associated with object surface physical composition, confidencelevel associated with the presence of the point cloud, confidence levelassociated with the angular surface orientation).

At step 2103, the at least one processor accesses classificationinformation for classifying a plurality of objects. For example, theclassification information may be stored on one or more memories. In oneexample, the classification information may comprise a database of knownfingerprints (such as reflectivity fingerprints, surface anglefingerprints, point cloud fingerprints, or the like).

At step 2105, based on the classification information and detectionsresults, the at least one processor classifies an object in thevehicle's surroundings. For example, step 2105 may be performed similarto step 1707 of method 1700, step 1805 of method 1800, step 1903 ofmethod 1900, or any combination thereof. In one example, the at leastone processor may classify a first object as a car based on theclassification information and on a plurality of first detectionsresults; classify a second object as a road based on the classificationinformation and on a plurality of second detections results; andclassify a third object as a person based on the classificationinformation and on a plurality of third detections results.

FIG. 22 depicts an example of identification performed using confidencelevels. For example, the identification depicted in FIG. 22 may bedetermined by executing method 1700, method 1800, method 1900, method2000, method 2100, or any combination thereof. In the example of FIG.22, two portions of high confidence are located on either side of aportion with low confidence. The processor may identify the objectaccordingly (e.g., as a vehicle). The processor may further use relatedconfidence scores to determine the boundaries of the identified object.

Accordingly, the example of FIG. 22 depicts how confidence levels may beused for clustering of detection points into objects and forclassification and/or other decision regarding such complex objects. Inthe example of FIG. 22, the processor may connect the twohigh-confidence separated points even though there is a low confidencepoint between them. Other objects may be clustered and connected similarto the example of FIG. 22.

Although the above examples are described with respect to the use ofconfidence levels, the same embodiments may be employed with respect toother data from the LIDAR system. For example, noise levels, ambientlight levels, confidence levels, determined velocity (or other changesin positions and/or size of detected objects), changes in signals orother variables (such as those listed above) across frames, or anycombination thereof may be used as fingerprints. In such embodiments, asdiscussed above, this other data may also be incorporated into a pointcloud map with distance information from the LIDAR system.

In another example, systems of the present disclosure may perform objectidentification and/or classification using at least two measurements, atleast three measurements, or at least four measurements of: a presenceindication, a surface angle, object surface physical composition, and areflectivity level. Any of the above combinations may additionallyincorporate changes in any of the measurements as compared with one ormore previous frames (e.g., changes in reflectivity, changes inconfidence levels, changes in ambient light, or the like).

Embodiments described above (e.g., embodiments utilizing one or moremeasurements, confidence levels, derived measurements, etc.) may beapplied to (and improve) other computer vision processes. For example,detection/clustering (e.g., object level from point cloud points)determining bounding boxes of objects, classification of objects/objecttype, tracking of objects (e.g., between frames), determining objectcharacteristics (e.g., size, direction, velocity, reflectivity, etc.),and the like may all have increased accuracy (and/or efficiency,depending on the implementation) by incorporated additional data such asother measurements and/or confidence levels, as described above.

In some embodiments, confidence levels (also referred to as“detection-quality values”) associated with different detections may beused as part of the considerations of whether or not to include suchdetections in the point cloud (or other three-dimensional model). Theconfidence values may be used together with other parameters pertainingto the same detection and/or other parameters pertaining to otherdetections. For example, the processor may be configured to selectivelygenerate point-cloud point based on information of reflected lightinformation of a detection only if the detection information of thedetection complies with decision criteria based on a confidence levelassociated with the point and optionally with at least one additionalcondition. The at least one additional condition may relate to any ofthe other one or more types of information determined for the point(e.g., reflectivity, surface angle, ambient light level, noise level,material properties, etc.). The at least one option may also relate tospatial relationships to other detection locations. For example, the atleast one processor may decide that, even though points are generallyadded to the point cloud model having confidence values above 50%, ahigher confidence value (e.g., 75% or more) may be required fordetections isolated from other detections (e.g., detections whosedistance to the other nearest detection is at least a predetermineddistance R, such as 2 meters). In another example, detections withrelatively low confidence value may nevertheless be included in thepoint cloud if other parameters of the detection meet some decisioncriteria. For example, a detection with a relatively low confidencevalue but having similar characteristics to neighboring detections(e.g., also metallic and having a similar surface angle) may be includedin the point cloud despite its relatively low confidence value.

Referring to the previous examples, in any of the previous examples, theprocessing of the point cloud may optionally further include using theone or more confidence values associated with a point cloud point (or apixel) to determine whether to use a specific point cloud point or notand/or how to use the point. Moreover, the processing of the sensor datato generate the point cloud may include determining whether or not toeven include such point-cloud points from the beginning, e.g., based onthe at least one confidence value and optionally on additionalinformation (such as the spatial relationships with other points). Thegeneration of the point cloud may be executed by the same processor (orsub-processing module) utilizing the point cloud for the detection ofobjects, but different processors (or processing modules) mayadditionally or alternatively be used for those different tasks. Anycombination of one or more of the aforementioned parameters may be usedin the decision of whether or not to include a detection in the pointcloud, even without basing the decision on a confidence value associatedwith the detection (if any). For example, the at least one processor maybe configured to selectively generate point-cloud point based oninformation about reflected light information of a detection but only ifthe detection information complies with decision criteria based on anyone or more of the following: reflectivity, surface angle, ambient lightlevel, noise level, material properties, spatial relationships to otherdetection locations, parameters of such other detections, any one ormore confidence values associated with the detection and/or withneighboring detection, operational settings of the LIDAR during thedetection, and so on. The utilization of such parameters when generatingthe point cloud may save computational resources and time during objectdetection, identification, and classification.

Although the above examples are described with respect to the use ofconfidence data for classification, confidence data may also be used inother processes, such as clustering, boundary determination, and thelike. For example, points with similar (e.g., within 2%, within 5%,etc.) confidence levels may be grouped together the processor. Thisclustering may allow for the processor to direct more light flux towardsclusters with lower confidence levels rather than towards clusters withhigher confidence levels, ensuring that power is more efficientlydistributed. In another example, a group of points with similar (e.g.,within 2%, within 5%, etc.) confidence levels may form an outline thatthe processor may determine to be the boundary of an object. Theprocessor may determine the boundary even when points within theboundary have confidence levels that differ significantly from theconfidence levels on the boundary.

In any embodiments described above, thresholds may be set at the levelof the processor (e.g., after receipt of measurements from one or moresensors) and/or at the level of the sensor(s) (e.g., by configuring thesensor such that measurements below one threshold and/or above anotherthreshold are not detected).

Optionally, a processor of a LIDAR based system (e.g., processor 118 orthe like) may utilize different representations of locations ofpoint-cloud points (or, more generally, locations in space determined byprocessing of the reflection signals of the LIDAR system) in order todetect objects in the FOV of the LIDAR and/or to assess differentcharacteristics of such objects. In particular, the processor mayutilize different coordinate systems for different types ofcomputations. The points of the point cloud may be indicated indifferent coordinates systems, such as Cartesian (x,y,z), spherical(r,θ,ϕ), cylindrical (ρ,θ,z), system specific (e.g. (Pixel-ID,Distance)), and so on. The error in location for each point of the pointcloud may differ in different coordinates. For example, considering agiven location whose position is provided in spherical coordinates, theerror in angles (θ and/or ϕ) may depend on the accuracy of positioningof the at least one deflector (or at least the accuracy of determiningthe position of the deflector), while the error in distance (R) maydepend on the duration of the illumination pulse, on the accuracy of thetime-of-flight analysis, and like factors. Likewise, different systemconsiderations may affect errors in other axes of other coordinatesystems (e.g. x,y,z,ρ).

In one example, a processor of a LIDAR based system (e.g., processor 118or the like) may be configured to utilize Cartesian coordinates for thedetection locations to determine presence of objects in differentlocations, for determining of bounding boxes of objects, etc. Theutilization of the Cartesian coordinates may be used, for example, inorder to easily sum (or otherwise combine) information from differentlocations which are substantially located one above the other (e.g.,differing only in their z coordinate value).

In another example, a processor of a LIDAR based system (e.g., processor118 or the like) may be configured to utilize cylindrical coordinatesfor the detection locations to determine presence of objects indifferent locations, for determining of bounding boxes of objects, etc.The utilization of the cylindrical coordinates may be used, for example,in order to easily sum (or otherwise combine) information from differentlocations which are substantially located one above the other (e.g.,differing only in their z coordinate value).

In a third example, a processor of a LIDAR based system (e.g., processor118 or the like) may be configured to utilize Cartesian coordinates forthe detection locations to compute an angle of a surface of an object inthe FOV based on the location of several points of the point cloud. Thecomputation may include determining for each point a z-axis error from ahypothetical plane, and finding a plane which minimizes the collectivez-axis errors of all data points (e.g., least means square).

In a fourth example, a processor of a LIDAR based system (e.g.,processor 118 or the like) may be configured to utilize cylindricalcoordinates for the detection locations to compute an angle of a surfaceof an object in the FOV based on the location of several points of thepoint cloud. The computation may include determining for each pointz-axis distances (or “z-axis errors”) from different hypotheticalplanes, and finding a plane which minimizes the collective z-axisdistances of all data points (e.g., least squares, maximum likelihoodestimation, or the like). The angle of the selected plane may beselected as the best matching angle for a surface of the object.

In a fifth example, a processor of a LIDAR based system (e.g., processor118 or the like) may be configured to utilize spherical coordinates forthe detection locations to compute an angle of a surface of an object inthe FOV based on the location of several points of the point cloud. Thecomputation may include determining for each point R-direction distances(or “R-direction errors”) from different hypothetical planes (e.g., thedistance along the line connecting the point to the origin of axes), andfinding a plane which minimizes the collective R-direction distances ofall data points (e.g., least squares, maximum likelihood estimation, orthe like). The angle of the selected plane may be selected as the bestmatching angle for a surface of the object.

With regards to the minimization of the collective errors, the estimatorfunction may be a weighted estimation function which in addition tobeing a function of the error (e.g., squared error, etc.) may also beweighted for any one or more of the following examples: distance fromthe origin of axis (R) or from another predetermined location; for aprojection of that distance on a plane (e.g. p) or from anotherpredetermined location; for a projection of that distance on a plane;for an angle of the point (e.g. ϕ, θ), etc. Any form of a weightedfunction may be implemented by one of ordinary skill in the art.

Movement Based Classification in LIDAR

FIG. 23A is a block diagram of a system for processing cloud pointinformation to identify objects in a scene, in accordance with examplesof the presently disclosed subject matter. It is noted that any of thecomponents of the systems disclosure in this application may beimplemented in hardware, software, firmware, or any combination of theabove. Any component may be implemented on a single processor or on morethan one processor, and different components may be implemented on acommon processor (or processors). In addition to components of thesystem (illustrated in rounded rectangles), the system also illustrateddata flow in the system (illustrated within parallelograms). Optionally,LIDAR system 100 may serve as the LIDAR system of FIG. 23A. The systemsdisclosed throughout the present disclosure may be used to processinformation generated by a LIDAR (or a plurality of LIDARs), based ondetection information by light sensitive sensors of the LIDAR. In suchcase, the processor(s) on which the system components are implementedmay be part of the Lidar (e.g. included in the same casing as thesensors), or external thereto (e.g. processor(s) located on avehicle—e.g. car or any type of autonomous vehicle-on which therespective LIDAR(s) is installed).

The system receives point cloud (PC) model information as an input. Thepoint cloud model includes information for a plurality of 3D locationpoints (a point whose location corresponds to a location in a threedimensional space). The point cloud may include different types ofinformation for each point (e.g. location, intensity, reflectivity,proximity to other points of the point cloud, etc.). Optionally, othertypes of input may also be provided to the system, in addition to or inlieu of the PC information. It is noted that other types of 3D modelsmay be used instead of (or in addition to) PC models, such as polygonmesh and range maps. While the discussion below refers to point cloudmodels as a primary implementations—for reasons of brevity and clarityof the disclosure—it is noted that any of the systems, methods, andcomputer code programs discussed below may be implemented for any othertype of 3D model, mutatis mutandis.

The PC information may be provided to a feature extraction module(denoted “feature extraction” in the drawings), which may process the PCinformation to provide a plurality of feature images. Each of thefeature images may include data which emphasize or provide informationregarding different aspects of the point cloud and/or of objects in thescene whose detection is represented in the point cloud (e.g., cars,trees, people, and roads). Optionally, the feature images may be of thesame resolution of the point cloud model (i.e., having the same numberof data points, optionally arranged into similar sized 2D arrays). Thefeature image may be stored in any kind of data structure (e.g., raster,vector, 2D array, 1D array). Optionally, the feature extraction modulemay be implemented using a neural network (e.g., a convolutional neuralnetwork (CNN)). Nevertheless, other data processing techniques may beused instead. The feature images outputted by the feature extractionmodule may be further processed by a Region Proposal processing module(e.g., a Region Proposal Neural Network), which processes them toprovide identified region(s) of interest. The output region of interestsmay be regions of the point cloud model (or any image derived therefrom)which the RPN determined may include an object (i.e., the correspondingscene segment—whether 2D or 3D—is likely to include an object, such as acar or a person).

The feature images and the identified region(s) of interest may be usedby a Region-of-Interest (ROI) extraction module to extract from thefeature images information from the identified region(s) of interest.For example, the feature extraction module may output 100 featureimages, each including, e.g., 200×50 pixels, of which the ROI extractionmodule may extract one or more series of, e.g., 100 (or less) 8×8 pixelpatches—each series corresponding to a single object (or imageinformation suspected as an object). Optionally, the ROI extractionmodule may be implemented using a neural network (e.g., a CNN).Nevertheless, other data processing techniques may be used instead. Theinformation extracted by the ROI extraction module may be provided to aclassification module (which may optionally also serve as a regressionmodule), which may process this information to determine aclassification for the suspected object (e.g., “car,” “headlight,”“person,” “tree,” “road,” “false detection,” “error,” “unknown”).Optionally, the classification module (or another module of the system)may process the same information to provide a bounding box for thesuspected object (e.g., coordinates of a cuboid or another polyhedronwhich contains the object). It is noted that the classification and/orbounding box may be provided to an object which combines severalsuspected objects. For example, the detection information may onlyinclude detection of headlights of a car, but the bounding box mayinclude the entire car, and be classified as a “car.”

FIG. 23B is a block diagram of system 4100 for processing cloud pointinformation to identify objects in a scene, in accordance with examplesof the presently disclosed subject matter. In addition to components ofthe system (illustrated in rounded rectangles), FIG. 23B alsoillustrates data flow in the system (illustrated within parallelograms).Optionally, LIDAR system 100, the host LIDAR system, or combination ofthese two systems may serve as the LIDAR system 4100. On top of thecomponents and the processing discussed with respect to the system ofFIG. 23A, system 4100 may use information deriving from two or morepoint cloud models, which may be generated from detection informationcollected in different times (non-overlapping or partly overlapping).For example, the LIDAR may generate 25 “frames” per second (for each oneof these “frames” a PC is generated), and LIDAR system 4100 may use PCof two or more different frames (consecutive or not) for detectingand/or classifying an object(s) in the scene observed by the Lidar. Asdiscussed below, the detection and/or classification of the object(s)may be based on differences between representation of the object(s) inthe two models, on movement of the object(s) between the two models, orin any other way.

In addition to the components discussed with respect to the LIDAR systemof FIG. 23A, LIDAR system 4100 may also include a storage unit forstoring extracted RO information (denoted “Extracted ROI Storage”), sothat extracted ROI information of different models may be used for theclassification of objects. LIDAR system 4100 may include at least onecomponent (denoted in the example of FIG. 23B as “Comparator/Offset”module), which may processor ROI extracts (also referred to as “clipouts”) extracted (“clipped”) from two or more different models toprovide information, which may than be fed to the classification module,for detections and/or classification of one or more objects in thescene. The aforementioned “comparator” module may apply different typesof processing/analysis to: (a) first object detection derived from thefirst 3D model and/or (b) second object detection in the second 3Dmodel.

For example, the “comparator” module may: (a) determine differencesbetween representation of the object(s) in the two models; (b) determineoffset between locations of the object (or of an identifiable partthereof) in the two representations; (c) determine translationparameters of the object (or of an identifiable part thereof) in the tworepresentations (e.g. rotation angles, expansion); (d) determine speedof the object (velocity and/or direction). Optionally, the “comparator”module may be implemented using a neural network (e.g., a CNN).Nevertheless, other data processing techniques may be used instead.

FIG. 24 is a flowchart illustrating computer-implemented method 4500 forobject classification, in accordance with examples of the presentlydisclosed subject matter. Method 4500 may include, executing on at leastone processor, any combination of two or more of the following steps:

-   -   a. Step 4502: processing first LIDAR detection information to        provide a first 3D model;    -   b. Step 4504: processing second LIDAR detection information,        obtained after the first LIDAR detection information, to provide        a second 3D model;    -   c. Step 4506: identify a first object detection in the first 3D        model (step 4506 can be executed before, concurrently with, or        partly concurrently with step 4504);    -   d. Step 4508: identify a second object detection in the second        3D model (step 4508 can be executed before, concurrently with,        or partly concurrently with step 4506); and    -   e. Step 4510: based on the first object detection and the second        object detection, determine a classification for an object        detected by the LIDAR.

Optionally, system 4100 may be able to determine the presence of objectsthat cannot be detected (at least with a required confidence level) byother LIDAR systems, such as the LIDAR of FIG. 23A. Optionally, system4100 may be able to classify objects which cannot be classified (atleast with a required confidence level) by other LIDAR systems, such asthe LIDAR of FIG. 23A. For example, LIDAR reflections from a relativelyremote cyclist may only provide three or four points in the point cloud,which may be insufficient to identify that this is a cyclist (and not aperson, a runner, or a tree). However, using information from two ormore different PC models, obtained in different times, system 4100 maybe able to determine, based on the velocity and direction of the object,that a proper classification of that object is as a “cyclist.” The term“object” is known in the art of computer vision and should be construedto include any potentially moving entity within a field of view (FOV) ofthe LIDAR.

Method 4500 and/or system 4100 may implement any one or more of thefollowing: (a) the use of movement classification and regression ofevery region proposal to use movement features as well; (b) associationbased on CNN feature outputs; (c) after association, combining featuresfrom previous and current frames; (d) concatenating region offset in XYplane to CNN output features; (e) classification and regression based oncombined features; and (f) adding a term to the loss function such thatthe same object in different frames is in similar feature space.Optionally, system 4100 may implement any variation and/or aspect of theembodiments discussed with respect to method 4500. Similarly, method4500 may implement any variation and/or aspect of the embodimentsdiscussed with respect to system 4100.

Referring to the LIDAR systems of FIGS. 23A and 23B, it is noted thatsuch a system may be integrated into the LIDAR (or one of the LIDARs)providing detection information to the system. Alternatively, such asystem may also be integrated into a host (e.g. a vehicle) on which theLIDAR is installed. Optionally, such a system may be remote from theLIDAR (e.g., on a remote computer or server).

Referring to method 4500, method 4500 may optionally include processingthe first object detection information and/or the second objectdetection information for assessing a speed of the corresponding objectbased on a smear and/or blurring resulting from movement of the objectduring the capturing of the corresponding LIDAR detection information.

The classification of the object in step 4510 may be further based onthe smear and/or blurring. Alternatively, classification of an objectbased on blurring and/or smearing detected in object detectioninformation from a single frame of the LIDAR may also be implemented,mutatis mutandis. The LIDAR providing detection information to the LIDARsystem of FIGS. 23A and/or 23B may analyze a changing scene todetermine/detect scene elements. When used in conjunction with anoptional host (such as a vehicle platform and/or a drone platform), theLIDAR may provide a detected scene output. The optional host device mayutilize a detected scene output or signal from the scanning device toautomatically steer or operate or control the host device. Furthermore,the LIDAR may receive information from the host device and update itsoperational parameters accordingly. The LIDAR systems of FIGS. 23A and23B may be scanning or non-scanning LIDARs. Optionally, LIDAR system4100 may include at least one processor executing any one or more of themethods discussed above. Likewise, the movement-based (or, moregenerally, changed-based) classification discussed with respect to LIDARsystem 4100 and method 4500 may be implemented in any one of the systemsand methods discussed above.

Detecting object surface composition in LIDAR

Certain materials—like metal, glass, and retro-reflectors—have highdegree of specular reflection and maintain polarization of the light.Other materials, in comparison, do not maintain polarization. Therefore,LIDAR systems of the present disclosure may use polarization ofreflected light to identify and/or classify objects in its field ofview. For example, for vehicles, polarization of illumination may bepreserved on the vehicle's metal body, glass surfaces, etc. Road signsmay also be identified by the polarization characteristics of the retroreflectors they include.

A LIDAR system (such as LIDAR system 100, or any type of LIDAR system,whether scanning or not scanning) may have a light sensor configured todetermine polarization characteristics of reflected light signalsarriving from the field of view. Optionally, one or more of the at leastone light source of the LIDAR system may project, onto the FOV,polarized light. The at least one sensor may provide to the processor,not only the detections signals by which ranges of objects can bedetermined (e.g., time of flight information), but also informationabout polarization of light arriving from different parts of the FOV(e.g., from each pixel, or instantaneous FOV). The processor may beconfigured to process the information of the polarization associatedwith one or more detection signals (e.g., of one or morepixels)—optionally together with detection information and/or otherinformation, to provide any one or more of the following: (a)identification of one or more objects in the FOV; (b) classification ofone or more objects in the FOV (e.g., car, person, moving, still); and(c) additional data pertaining to one or more objects in the FOV (e.g.,dimensions, orientation, speed). The processor may apply any type ofalgorithm and/or hardware to achieve the above products, such asComputer Vision (CV), Machine Learning (ML), Convolutional NeuralNetwork (CNN), Deep Learning (DL), Look-Up Table (LUT), or the like.

The utilization of polarity by the LIDAR system may be implemented inmany ways. The following are but a few examples:

-   -   a. The at least one light source may project light having a        known state of polarization (SOP). The received light may be        split (e.g., using one or more beam splitters) into two        orthogonal SOPs (e.g., two linear orthogonal polarizations),        directed into two groups of detectors (or two groups of        detectors arrays), each group including at least one detector        (or detector array). The relative difference between the        received signals from the same target may be related to the        optical target properties such that metal (or other specular        objects) may result a large relative difference between the        signals of the two detectors, while for scattering objects        (e.g., stone walls, clothing, trees, etc.) the relative        difference may be small.    -   b. The at least one light sources may project light having a        known SOP. Received light may be directed into a single group of        one or more detectors (or at least one detector array) through a        polarization modulator (e.g., a photo-elastic modulator), where        one light pulse may be related to one SOP of the modulator, and        a second light pulse may be related to the second orthogonal SOP        of the modulator.    -   c. The LIDAR system may use two sets of light sources (each        including one or more light sources) having orthogonal SOPs and        activate them in alternating times to the same target (in the        same direction). Received light from the target may be directed        into a single group of one or more detectors (or at least one        detector array) with a polarizer in front of it. The signals        from the alternating light signals with alternating orthogonal        polarizations may be compared for the detection, classification        and/or analysis of the object.    -   d. A group of at least one light source may project light with        alternating orthogonal SOP (using, for example, a photo-elastic        modulator). Received light from the target may be directed into        one group of one or more detectors (or detector arrays) with a        polarizer in front. The signals from the alternating orthogonal        polarizations may be compared, and the results may be used by        the processor for the detection, classification and/or analysis        of the object.        MEMS Housing with Integrated Circuitry

In some embodiments, as noted previously, LIDAR system 100 may beincorporated onto a vehicle. Example embodiments of such incorporationare included in PCT Application No. PCT/IB2017/001320, filed Sep. 20,2017, which is incorporated herein by reference in its entirety. Due toengine operation and motion over roads and other surfaces, a certainamount of vibration may result, and this vibration may interfere withoperation of LIDAR system 100. For example, vibrations may betransferred to any of the components of LIDAR system 100 (e.g., lightsource and/or the light deflector) and may affect their performanceand/or the overall performance of the system. Vibrations in a LIDARsystem may also result from other causes (e.g., from a carrying platformother than a car, from wind, waves).

In addition to—or alternatively to—vibrations, LIDAR systems may alsosuffer from uncertainties in positioning of one or more deflectors(e.g., mirrors) or other components. For example, when using apiezo-electrically actuated MEMS mirror, the piezo-electric actuationmay include a certain amount of hysteresis, which means that a certaincontrol voltage may not necessarily result in desired positioning of themirror due to ambiguity in mirror position compared to controllingvoltage. Accordingly, a position feedback mechanism may be implementedin a LIDAR system in order to counter such effects (e.g., vibrations orother inaccuracies). Sensors of the position feedback mechanism may beused to obtain data indicative of position, orientation, velocity oracceleration of the at least one light deflector (and/or of anothercomponent of the LIDAR system). These determined data regarding thestate of the light deflector (or other components) may be determinedregardless of the reasons for diversions (e.g., vibrations, hysteresis,temperature effects) and/or may be used in the feedback control of thelight deflector to improve detection accuracy and operability of theLIDAR system (e.g., in the examples provided below).

As exemplified in FIGS. 25A and 25B, LIDAR system 100 may incorporatesystem 3200 (e.g., a vibration suppression system). In some cases, LIDARsystem 100 may determine the presence of vibration (and/or otherinaccuracies) and may take one or more actions to reduce or eliminatethe effects of such vibration/inaccuracy. LIDAR system 100 may determinethe presence of vibrations/inaccuracies using any suitable technique,e.g., using one or more sensors and/or one or more feedback mechanisms(e.g., as discussed below in greater detail).

In some cases, vibration/location inaccuracy may be detected based onfeedback received from deflector 114. For example, the vibrationsuppression system of LIDAR system 100 may respond to feedbackdetermined based on mirror position data associated with deflector 114(e.g., using mirror position feedback sensors illustrated in FIG. 25C todetect movements of deflector 114 resulting from vibration). In a LIDARsystem configured to suppress the effects of vibration or uncertaintiesin light-deflector position, system 3200 may include at least oneprocessor configured to control at least one light source in a mannerenabling light flux of light from the at least one light source to varyover scans of a field of view; control positioning of at least one lightdeflector to deflect light from the at least one light source in orderto scan the field of view; and obtain data indicative of vibrations of avehicle on which the LIDAR system is deployed. Based on the obtaineddata indicative of sensed vibration, the at least one processor maydetermine to the positioning of the at least one light deflector inorder to compensate for the vibrations of the vehicle.

The at least one processor may also implement the determined adjustmentsto the positioning of the at least one light deflector to suppress onthe at least one light deflector, at least part of an influence of thevibrations of the vehicle on the scanning of the field of view. LIDARsystem 100 may include (and/or may receive information from) any type ofsensor capable of measuring at least one characteristic of vibration oran effect of vibration, including, for example, force, acceleration,torque, strain, stress, voltage, optical deflections, etc. Such sensors3216, 3218, and/or 3219 may be connected to one or more processorsassociated with LIDAR system 100, either directly or indirectly, viawired or wireless connections and may communicate to the one or moreprocessors of the LIDAR system information indicative of the sensedvibration.

Processing unit 108 of LIDAR system 100 may cause deflector 114 to movein such a way that counteracts at least a portion of themovement/location inaccuracy imparted to deflector 114, light projectingunit 102, sensing unit 106, or any other component of LIDAR system 100affecting light projection, collection, or detection. For example, insome embodiments, processing unit 108 including one or more processors118 may monitor the position or orientation of deflector 114, comparethe monitored position with an intended instantaneousposition/orientation, and, if a difference is determined, may causedeflector 114 to move toward an intended instantaneousposition/orientation. Using such a feedback approach, processor 118 maycounteract effects of vibrations that tend to displace deflector 114from its intended position or orientation. In some embodiments,processor 118 may be configured to cause vibration-reducing orcancelling movements to any movable component of LIDAR system 100 inorder to mitigate the effects of sensed vibrations.

FIGS. 26A and 26B are side-view cross-sections illustrating MEMS mirror7002 enclosed within a housing 7004, in accordance with examples of thepresently disclosed subject matter. Housing 7004 may include window7006, having one or more transparent (or at least partly transparent)portions through which light can be transmitted to and from the mirror.The housing may enclose the MEMS mirror 9002.

Housing 7004 may be a sealed housing that may be manufactured usingwafer level packaging or any other technology. Housing 7004 may includea base 7008 made at least partly from silicon or another wafer material.Base 7008 may optionally include some transparent or semi-transparentparts. Optionally, the MEMS mirror may not be parallel to a window ofthe LIDAR system during any stage of its movement, e.g., for preventinginternal reflection by the MEMS mirror and the window, which maygenerate unwanted light artifacts. These light artifacts may beattenuated and even prevented by providing a window that is not parallelto the MEMS mirror or when the optical axis of the MEMS mirror and theoptical axis of the window are not parallel to each other. When eitherone of the MEMS mirror and the window are curved or have multiplesections that are oriented to each other, it may be beneficial that nopart of the MEMS mirror should be parallel to any part of the window.The angle between the window and the MEMS mirror may be set such thatthe window does not reflect light towards the MEMS mirror when the MEMSmirror is at an idle position or even when the MEMS mirror is moved byany of the actuators. The MEMS mirror assembly (comprised within housing7004) may optionally include additional layers of the MEMS structure,e.g., as part of the MEMS mirror, as part of it supports, actuationmodule, etc.

Different parts of the housing may be formed by wafer level packaging.For example, window 7006 may be made from a glass wafer (or another typeof transparent wafer) bonded to a wafer on which the MEMS mirror isimplemented. Base 7008 may be made from the same wafer as the MEMSmirror or on another wafer bonded to the wafer on which the MEMS mirroris implemented. The wafer of base 7008 is also referred to as “the basewafer” for convenience. Optionally, the frame may be implemented on thesame wafer as the MEMS mirror, on the base wafer, or on a combination ofboth. Optionally, an integrated circuit (IC) may form a bottom region ofthe housing (e.g., implemented on the base wafer).

An integrated circuitry (IC) implemented as part of housing 7004 (e.g.,as part of the base wafer) may be used for different uses and mayinclude one or more processors, one or more sensors, one or more lightsources (e.g., LED), one or more power assembly modules, or the like.Optionally, one or more such components may also be connected to the ICafter the manufacturing process of the base wafer (e.g., by gluing) andmay cooperate with the IC to serve the same function. Optionally, one ormore such components may also be implemented on the same wafer as theMEMS mirror, may be connected to the IC after the manufacturing processof the base wafer, and may cooperate with the IC to serve the samefunction. Some examples of types of modules which may be implementedusing IC on the base wafer include: feedback sensors (e.g., detectors),sensing circuits, mirror drivers, and feedback circuits, mirrorcontroller, or the like. The IC on the base wafer is collectivelydenoted 7010 in the illustrations, for the sake of simplicity of thediagram. Some non-limiting examples of the uses of IC 7010 are discussedbelow. It is noted that any variation of MEMS mirror (whether providedas an example above or not) may be implemented.

For example, IC 7010 may include a processor configured to determine aninstantaneous angular position (e.g., using θ, φ coordinates) of theMEMS mirror. The term “instantaneous angular position” refers to aninstantaneous position of the at least one deflector which causes lightto be deflected towards (and/or from) a given angular direction (e.g.,indicated by θ, φ). Such a determination may be based on at least one ofoptical measurements, capacitance measurements, piezo resistancemeasurements, dielectric constant measurement, and piezo polarizationmeasurements from one or more of the vibration sensors associated withthe vehicle or LIDAR system 100 (e.g., sensors associated with lightdeflector 114). Vibrations and inaccuracies of the position of the MEMSmirror may be detected using one or more sensors which may include, forexample, one or more accelerometers, strain gauges, or any other type ofsensor suitable for sensing vibration or at least one characteristic ofvibration.

All of these types of sensing and feedback may be implemented using IC7010. IC 7010 may also include parts (or all of) a steering unit usingfor instantaneous directional control of the MEMS mirror. IC 7010 mayalso include an electrically controllable electromechanical driver(e.g., an actuation driver of the mirror). Such an actuation driver maycause movement or power to be relayed to an actuator/cantilever/benderconnected to the MEMS mirror. For example, IC 7010 may include aprocessor configured to control a position of the MEMS mirror based onreceived outputs of one or more sensors included in IC 7010 and/orconnected to IC 7010. For example, IC 7010 may determine adjustments forcountering observed vibrations, which may include computing appropriateaxis (0, <p) parameter adjustments to move the MEMS mirror to anintended instantaneous position. In some cases, these adjustments mayinclude moving the MEMS mirror in order to compensate for computedacceleration, torque, strain, etc. determined based on outputs fromsensors associated with the vehicle itself. Optionally, one or moreprocessors implemented in IC 7010 may cooperate with any other processorof the LIDAR system to achieve different goals. For example, a processorexternal to housing 7004 may provide to IC 7010 operational parameters,instructions, or the like.

The orientation of the MEMS mirror may be monitored by illuminating thebackside of the MEMS mirror 7002. It may be beneficial to illuminate atleast one area of the MEMS mirror and to sense reflected light in atleast three locations. Optionally. LIDAR system 100 may include adedicated light source for illuminating the back side of the MEMSmirror. The dedicated light source (e.g., LED) may be located behind themirror (i.e., away from its main reflective sensor used for thedeflection of light from the at least one light source 112), e.g.,within housing 7004. Alternatively, LIDAR system 100 may include opticsto direct light onto the back side of the mirror. In some examples,light directed at the back side of the MEMS mirror (e.g., light of thededicated light source) may be confined to a backside area of the mirrorand prevented from reaching the main reflective side of the MEMS mirror.

The processing of the signals of the back side sensors may be executedby processor 118 and/or by a dedicated circuitry integrated into IC 7010positioned within housing 7004 of mirror 7002. The processing mayinclude comparing the reflected signals to different back side sensorswhich may be located within housing 7004 (e.g., as part of IC 7010 orotherwise implemented on and/or connected to the base wafer),subtracting such signals, normalizing such signals, etc. The processingof such signals may be based on information collected during acalibration phase. In some embodiments, illuminating a backside of theMEMS mirror may be implemented when the back of the mirror issubstantially uniformly reflective (e.g., a flat back, withoutreinforcement ribs). However, this is not necessarily the case, and theback of the mirror may be designed to reflect light in a patternednon-uniform way. The patterned reflection behavior of the back side ofthe mirror may be achieved in various ways, such as surface geometry(e.g., by including protrusions, intrusions), surface textures,differing materials (e.g., silicon, silicon oxide, metal), or the like.Optionally, the MEMS mirror may include a patterned back side, having areflectivity pattern on at least a part of the back surface of themirror configured to cast a patterned reflection of the back sideillumination (e.g., from the aforementioned back side dedicated lightsource) onto the back side sensors implemented on the base wafer. Thepatterned back side may optionally include parts of optional reinforcingelements located at the back of the MEMS mirror. For example, thereinforcing elements may be used to create shadows onto the backsidesensors at some angles (and/or to deflect the light to a differentangle) such that movement of the mirror changes the reflection on thesensor from shadowed to bright. Optionally, the processing of theoutputs of the backside sensors may account for a reflectivity patternof the backside (e.g., resulting from the pattern of the reinforcementribs).

In the examples of FIGS. 26A and 26B, a microelectromechanical (MEMS)mirror assembly is depicted, which includes:

-   -   a. a window for receiving light manufactured on a first wafer,    -   b. a microelectromechanical (MEMS) mirror for deflecting the        light to provide a deflected light, the MEMS mirror manufactured        on a second wafer on which the following components are        manufactured: a frame; actuators; and interconnect elements that        are mechanically connected between the actuators and the MEMS        mirror, wherein the second wafer is different than the first        wafer;    -   c. a housing base manufactured on a third wafer, the housing        base comprising an integrated circuitry (IC); and    -   d. a housing, comprising at least the housing base and the        window, wherein the MEMS mirror is enclosed in the housing.

Optionally, the IC may include at least one of: a sensor, a feedbacksensor, a sensing circuit, a mirror driver, a feedback circuit, and amirror controller. Optionally, each actuator may include a body and apiezoelectric element. Optionally, the third wafer may be different thanthe second wafer. Optionally, the housing may be a sealed housing. TheMEMS mirror may move with respect to the support frame to which it isconnected, and the movement may be induced by the actuators. Theactuators may be controlled by the IC, by an external processor, or by acombination of both. Any two or more of the optional embodimentsdescribed above may be combined.

Embodiments of the present disclosure may also be implemented in acomputer program for running on a computer system, at least includingcode portions for performing steps of a method according to disclosedembodiments when run on a programmable apparatus, such as a computersystem or enabling a programmable apparatus to perform functions of adevice or system according to the disclosed embodiments. Embodiments ofthe present disclosure may also be implemented in a computer program forrunning on a computer system, at least including code portions that makea computer execute the steps of a method according to the disclosedembodiments.

A computer program is a list of instructions such as a particularapplication program and/or an operating system. The computer program mayfor instance include one or more of: a subroutine, a function, aprocedure, a method, an implementation, an executable application, anapplet, a servlet, a source code, code, a shared library/dynamic loadlibrary and/or other sequence of instructions designed for execution ona computer system.

The computer program may be stored internally on a non-transitorycomputer readable medium. All or some of the computer program may beprovided on computer readable media permanently, removably or remotelycoupled to an information processing system. The computer readable mediamay include, for example and without limitation, any number of thefollowing: magnetic storage media including disk and tape storage media;optical storage media such as compact disk media (e.g., CD-ROM, CD-Retc.) and digital video disk storage media; nonvolatile memory storagemedia including semiconductor-based memory units such as FLASH memory,EEPROM, EPROM, ROM; ferromagnetic digital memories; MRAM; volatilestorage media including registers, buffers or caches, main memory, RAM,etc.

A computer process typically includes an executing (running) program orportion of a program, current program values and state information, andthe resources used by the operating system to manage the execution ofthe process. An operating system (OS) is the software that manages thesharing of the resources of a computer and provides programmers with aninterface used to access those resources. An operating system processessystem data and user input, and responds by allocating and managingtasks and internal system resources as a service to users and programsof the system.

The computer system may for instance include at least one processingunit, associated memory and a number of input/output (I/O) devices. Whenexecuting the computer program, the computer system processesinformation according to the computer program and produces resultantoutput information via I/O devices.

Also, embodiments of the present disclosure are not limited to physicaldevices or units implemented in non-programmable hardware but can alsobe applied in programmable devices or units able to perform the desireddevice functions by operating in accordance with suitable program code,such as mainframes, minicomputers, servers, workstations, personalcomputers, notepads, personal digital assistants, electronic games,automotive and other embedded systems, cell phones and various otherwireless devices, commonly denoted in this application as “computersystems.”

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other elements or steps then those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one or more than one. Also, the use of introductory phrases such as“at least one” and “one or more” in the claims should not be construedto imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to embodiments containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an.”The same holds true for the use of definite articles. Unless statedotherwise, terms such as “first” and “second” are used to arbitrarilydistinguish between the elements such terms describe. Thus, these termsare not necessarily intended to indicate temporal or otherprioritization of such elements. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to advantage.

While certain features have been illustrated and described herein, manymodifications, substitutions, changes, and equivalents will now occur tothose of ordinary skill in the art. It is, therefore, to be understoodthat the appended claims are intended to cover all such modificationsand changes as fall within the true spirit of the disclosed embodiments.

It will be appreciated that the embodiments described above are cited byway of example, and various features thereof and combinations of thesefeatures can be varied and modified. For example, listed embodiments andexamples, even when numbered, are not necessarily exclusive of eachother. Wherever possible, listed embodiments and examples may becombined in any appropriate manner.

While various embodiments have been shown and described, it will beunderstood that there is no intent to limit the embodiments by suchdisclosure and examples, but rather, it is intended to cover allmodifications and alternate constructions falling within the scope ofthe disclosed embodiments, as defined in the appended claims.

What is claimed is:
 1. A vehicle-assistance system for identifyingobjects in a vehicle's surroundings, the system comprising: at least oneprocessor configured to: receive point-cloud information originatingfrom a LIDAR configured to project light toward the vehicle'ssurroundings, wherein the point-cloud information is associated with aplurality of data points and each data point includes indications of athree-dimensional location and angular information with respect to areference plane; construct, from the received point cloud information, apoint cloud map of the vehicle's surroundings, wherein the point cloudmap is indicative of a shape of a particular object in the vehicle'ssurroundings and of angular orientations of at least two surfaces of theparticular object, wherein the angular orientations of at least twosurfaces of the particular object include a slope of at least one of theat least two surfaces of the particular object; access object-relatedclassification information; and identify the particular object based onthe angular orientations of the at least two surfaces of the particularobject from the point cloud map and the object-related classificationinformation.
 2. The vehicle-assistance system of claim 1, whereinidentifying the particular object includes classifying a plurality ofpixels as being associated with the particular object.
 3. Thevehicle-assistance system of claim 1, wherein identifying the particularobject includes determining a type of the particular object.
 4. Thevehicle-assistance system of claim 1, wherein the point cloud map isfurther indicative of a reflectivity level of different portions of theparticular object.
 5. The vehicle-assistance system of claim 1, whereineach data point in the point cloud map includes measurements of at leasttwo of: a presence indication, a surface angle, a reflectivity level,velocity, and ambient light.
 6. The vehicle-assistance system of claim5, wherein the received point cloud information further includesconfidence level for each of the measurements.
 7. The vehicle-assistancesystem of claim 1, wherein the at least one processor is furtherconfigured to determine a match by identifying a most likelythree-dimensional representation in the classification information thatcorresponds to the information in the point cloud map.
 8. Thevehicle-assistance system of claim 1, wherein when a certainty level isunder a threshold, the at least one processor is further configured torequest additional information from the LIDAR.
 9. The vehicle-assistancesystem of claim 1, wherein the at least one processor is furtherconfigured to determine that the identified particular object is anobject of interest and to forward the LIDAR an indication of a region ofinterest that includes the identified particular object.
 10. Thevehicle-assistance system of claim 1, wherein identifying the particularobject is further based on the shape of the particular object from thepoint cloud map.
 11. A non-transitory computer-readable storage mediumstoring instructions that, when executed by at least one processor,cause the at least one processor to perform a method for identifyingobjects in a vehicle's surroundings, the method comprising: receivingpoint-cloud information originating from a LIDAR configured to projectlight toward the vehicle's surroundings, wherein the point-cloudinformation is associated with a plurality of data points and each datapoint includes indications of a three-dimensional location and angularinformation with respect to a reference plane; constructing, from thereceived point cloud information, a point cloud map of the vehicle'ssurroundings, wherein the point cloud map is indicative of a shape of aparticular object in the vehicle's surroundings and of angularorientations of at least two surfaces of the particular object, whereinthe angular orientations of at least two surfaces of the particularobject include a slope of at least one of the at least two surfaces ofthe particular object; accessing object-related classificationinformation; and identifying the particular object based on the angularorientations of the at least two surfaces of the particular object fromthe point cloud map and the object-related classification information.